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We noticed that some readers thought our Leica M11 Monochrom samples were a bit dark. Well, part of that comes down to the fact that many of them were captured at twilight, but the lack of color can make that difficult to suss out. These monochrome-only cameras should also be underexposed in order to protect data in the too-easily-blown highlights.

Nevertheless, we ran a handful of DNGs through Adobe Camera Raw and brightened them up just a tad – mostly exposure, shadows, and highlights – and you can see these samples at the top of the gallery. And as always, if you're really curious about the potential in these images, you can download the Raw files and edit them yourself.

View our updated Leica M11 Monochrom sample gallery

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So far in my landscape photography series, I've talked about compositional elements, their weights and how to use their properties to balance the composition by imagining a balance of torques around the middle axis of an image. I also discussed balancing of negative space, the perception of subject direction and the often-overlooked importance I reserve to the separation of elements. I then discussed the perception of depth and how to use sky in a landscape image.

To start this article, I'd like to go back to the elements of composition, and talk more about tying compositional components together, in hope that it ties all the points I made in my seven prior stories together.

Tsingy Rouge, Northern Madagascar. Canon 5D III, Canon 11-24mm F4 11mm | ISO 400 | 1/80 sec

Remembering some of the previous articles, we know that compositional masses counterbalance each other. If we arrange the elements well, these masses will be separated without overlapping, and have proper negative space around each of them proportional to their compositional weights. All this might sound like it means that the different elements of a composition are disconnected. But in a good image, compositional elements are anything but disconnected. I'd like to solve this ostensible discrepancy by explaining how compositional masses can be tied together using shapes and lines.

The Dunes of Sandwich Harbor meet the Atlantic Ocean, Namibia. Canon 5D III, Canon 70-300mm F4-5.6

Even when acknowledging the need to balance the masses around the central axis, I would claim that that alone often isn't enough to determine the best arrangement of the frame. There are many degrees of freedom, in the sense that there are many compositions in which the masses are arranged in a way that satisfies the balance we so desire. There is room, therefore, to consider additional ideas for arranging elements relative to each other in a way that's most pleasing to the viewer's eye.

What is it, other than the main masses being well-balanced around the central axis, that makes the different elements in this image work together as a whole and not as differentiated points of interest?

The first of these ideas is that we should consider the overall shapes created by the main elements (masses and lines) in a composition. To do that, we can imagine the image as devoid of any information other than these elements, and create a sort of mental diagram (or graph) depicting the interactions between them. The aim should be for the main elements to create some sort of flow, a continuum that makes sense to the eye and to the mind; this contributes to making the viewer connect with the photographer's feelings and vision when creating the image.

A very straightforward example of this is framing. When some of the elements in the composition form a frame around others, they form a connection: the frame accentuates what's inside it, focusing the viewer's eye on it and giving it more importance.

This beautiful rock arch forms a frame to focus the viewer's eye on the mountain and double rainbow. Spitzkoppe, Namibia. Sony A7R, Canon 16-35mm F2.8 II ISO 100 | 1/10 sec | F11

This glacial cave opening framed the background mountains, but also has the shape of a bird. Mýrdalsjökull, Iceland. Sony A7R, Canon 17mm F4 TS-E 3-shot pano at ISO 100 | 1 sec | F14

Here, the reflection is framed, enhancing the viewer's awareness of its contents and giving it more prominence and importance. Campo Poincenot, Parque Nacional Los Glaciares, Argentinean Patagonia. Canon 5D IV, Canon 16-35mm F2.8 III Focus stack at ISO 100 | 1/25sec | F16

But the shapes you can compose out of the main compositional elements are not limited to frames. There are many more examples. S-curves, which connect the main masses with winding lines, also come to mind.

There is a clear S-curve guiding the viewer's eye from the top left to the right, left and again to the right. This unconcious tour of the image makes it more striking to the viewer. Tupiza, Bolivia. Canon 5D III, Canon 70-200mm F4 ISO 200 | 1/40 sec | F13

Here, the S-curve is not present in the image, but the arrangement of masses creates an imaginary connection is the shape of an S. The effect is almost the same. Flakstad fjord, The Lofoten Islands, Arctic Norway. Canon 5D IV, Canon 11-24mm F4 Focus stack at ISO 100 | 1/20 sec | F11

The benefit of an S-curve is that its shape (real or imaginary) winds back and forth, causing the viewer to consider different areas in the composition and creating a connection between them. It also encourages the viewers eye to wander back and forth in the image, giving them the pleasure of exploring it.

The shorelines of the Dead Sea wind back and forth between left and right. Ein Gedi, Israel. DJI Mavic II Pro ISO 200 | 1/20 sec | F4

Shapes created by compositional elements can vary. In the image below there is a very nice multi-pyramid shape: not only is the mountain shaped like a pyramid, but the lines of its sides, when continued, form another pyramid with the island as its base, and then yet another pyramid with the foreground trees.

Let's draw the diagram, just to show this more clearly:

I claim that having this sort of extra connection between the different elements enhances an image a great deal.

Another example is an image I've already discussed in the series.

A top view of Fagradalsfjall Volcano, Iceland, April 2021. DJI Mavic 2 Pro pano stitch 1/30 sec | F7.1

In this image we have several concentric circles, connected by radial lines. The star shape draws the eye into the central subject (the eruption and the pool) and connects it to the outer layers.

Hopefully the ideas and shapes above convince you of the importance of connecting the elements of your image in more than one way. To continue, I'd like to show you another pyramid shape, and ask you what is it about the main elements, other than this shape and the balance around the central axis, that contributes to the composition.

A clear pyramid shape ties the main compositional mass to the foreground using lines. What property do these lines possess that enhances the composition further? Deadvlei, Namibia. Canon 5D IV, Canon 16-35mm F2.8 III ISO 100 | 1/100 sec | F16

To hint at what I have in mind, here is a comparison between 2 images from the same place: Skagsanden Beach in the Lofoten Islands.

I claim that these images, though superficially similar, differ in a very important trait. In the image on the left, the lines lead toward a compositional mass, whereas in the image on the right, the lines lead away from the background subject.

Lines are powerful tools in composition. As discussed above, they can be used to connect different compositional masses, creating a composition that works as a whole. But even more importantly, lines are a tool to create depth.

In previous articles I talked about the sense of depth and how important it is for a landscape image. Using wide-angle lenses, separating elements, correct use of negative space – all these contribute to the feeling of depth. But lines can perhaps be more powerful than all of them in making the viewers feel like they are inside the world depicted in the image.

This is not a scientific fact, rather a gut feeling, but I think that when a line connects foreground and background it makes the viewer subconsciously compare them, going back and forth and thus emphasizing the distance between them. A line can also enhance the main compositional masses simply by being an arrow, either pointing to them or emanating from them. Let's look at a few examples of lines connecting and/or emphasizing masses.

	The curved bank of this glaciel morraine connects the foreground rock and the background mountains. It also enhances the stark difference in lighting between them. Torres Del Paine, Chilean Patagonia. Canon 5D IV, Canon 16-35mm F4 IS Focus stack at ISO 100 | 0.5 sec | F14
	Here the main line connects the mountain to the foreground subject, the intersection of several cracks in the frozen lake. It then continues down to also connect to the bottom of the frame. Lake Tasersuaq, Uummannaq, Greenland Canon 5D IV, Canon 11-24mm F4 Focus stack at ISO 100 | 1/8 sec | F10
	Here, the lines don't lead to the subject but rather emanate from it. This gives these rather small icebergs a heavier compositional weight, blowing them up in importance and making them able to capture the viewer's eye despite their small size. Uummannaq Fjord, Greenland. Canon 5D IV, Canon 11-24mm F4 Focus stack at ISO 1600 | 3.2 sec | F4

Finally, I'd like to express how important it is when leading lines connect to the bottom of the image. This adds to the sensation of depth, but more importantly it connects the viewer to the scene, making them feel like part of the depicted world.

The stream, the main leading line in this image, comes from the bottom of the image, making the viewers feel like they are standing in the flowing water. Skagsanden Beach, The Lofoten Islands, Arctic Norway. Canon 5D IV, Canon 11-24mm F4 ISO 3200 | 8 sec | F4

It's good to study a counter example to this. Consider the image below.

Paine Grande in morning light. Torres Del Paine, Chilean Patagonia. Sony A7R, Tamron 24-70mm F2.8 40mm | ISO 200 | 1.6 sec | F13

The mountain looks nice with the red sunrise light, which also reflects on the river. Overall I'd say the composition is nicely balanced, even if not spectacular. But my main problem here is that the river does not go to the bottom of the frame, but rather sideways. This flattens the image and compresses it in an unappealing way.

I hope you've enjoyed my rather unorthodox ways of thinking about composition. I've said this a thousand times but I will say it again: this was not a guide on how to compose in the field, but rather an exploration of different ways of understanding why certain images work and other don't. Take what you wish from it – the important thing is that you understand that composition is the single most important thing in an image. Because nothing will ever change that.

---

Erez Marom is a professional nature photographer, photography guide and traveller based in Israel. You can follow Erez's work on Instagram and Facebook, and subscribe to his mailing list for updates and to his YouTube channel.

If you'd like to experience and shoot some of the world's most fascinating landscapes with Erez as your guide, take a look at his unique photography workshops in Greenland, Madagascar, Namibia, Vietnam and the Argentinean Puna.

Erez also offers video tutorials discussing his images and explaining how he achieved them.

More in The Landscape Composition Series:

• Part 1: Masses and Lines
• Part 2: Balancing the Weights
• Part 3: Negative Space
• Part 4: Subject Direction and Lead Room
• Part 5: Separation of Elements
• Part 6: Depth
• Part 7: The Thing about Sky

Selected Articles by Erez Marom:

• On Originality in Landscape Photography
• Lava Frenzy: Shooting Fagradalsfjall Volcano
• Parallelism in Landscape Photography
• Winds of Change: Shooting Changing landscapes
• On Causality in Landscape Photography
• Shooting Kīlauea Volcano, Part 1: How to Melt a Drone
• The Art of the Unforeground
• Whatever It Doesn't Take
• Almost Human: Photographing Critically Endangered Mountain Gorillas

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This image was captured with a Fujifilm X-T5 at ISO 12,800 and processed using DxO's DeepPRIME XD denoising algorithm.

It's been a little over a decade since Fujifilm launched its first camera with an X-Trans sensor, the Fujifilm X-Pro1, way back in January 2012. With a color filter array that samples red, green and blue pixels in every horizontal line and vertical column, X-Trans sensors are designed to more evenly sample the color information across the image. This, in turn, allows better resistance to color moiré versus the more traditional Bayer filter, which skips either the red or blue pixels in every other line or column.

The downsides of the X-Trans approach are twofold. With a larger 6x6 pattern rather than the 2x2 of Bayer, it makes the task of demosaicing the color data not just unique to Fujifilm cameras – requiring software makers to implement more custom code – but also more processor-intensive and challenging to optimize. And French software maker DxO's equally unique approach of denoising prior to demosaicing further complicated the situation. So for years it simply eschewed X-Trans support altogether, offering Fujifilm camera support only for its Bayer-based models.

The denoising samples throughout this article were made from these five images, shown here in their out-of-camera JPEG versions. You can click each crop in the rollover galleries below to open the full-sized, DxO-processed JPEG.

In October 2021, that finally started to change with the introduction of DxO's PhotoLab 5. That brought basic X-Trans support, although a couple of gaps remained in which noise reduction algorithms were available for it. The recent arrival of PhotoLab 6.4 has now closed the biggest of these, bringing support for the company's top-of-the-line DeepPRIME and DeepPRIME XD denoising algorithms to Fujifilm X-series shooters. (This leaves just the company's standard PRIME denoising algorithm and a handful of more minor features off the table for your X-series camera.)

So how does DeepPRIME get on with X-Trans imagery in the real world? Let's roll up our sleeves and take a closer look, courtesy of a selection of high ISO shots from our recent Fujifilm X-series reviews!

All sample images and their crops in this article were converted from raw using the just-released PhotoLab 6.5 update (also available for macOS) with its "DxO Optical Corrections only" preset, and each denoising algorithm was used at its default settings.

Test 1: Bread (Fujifilm X-T4, ISO 2500)

No denoising	HQ denoising	DeepPRIME	DeepPRIME XD

We'll start off with a shot from our Fujifilm X-T4 review at ISO 2500, a moderately high sensitivity. As you can see, with no denoising applied the noise intrudes quite a bit and, despite the X-Trans color filter array, there's still a fair bit of false color artifacting on display too.

The base HQ denoising algorithm cleans things up considerably, but it also robs some detail from the finer flour particles stuck to the bread, and the cracking in the crust looks a bit soft at 1:1 resolution.

And while DeepPRIME crispens things up, the newly-available DeepPRIME XD algorithm definitely does a much better job. Its result is even crisper, and it restores more of the fine details, yielding a less plasticky feel than the standard DeepPRIME.

Test 2: Duck (Fujifilm X-S10, ISO 3200)

No denoising	HQ denoising	DeepPRIME	DeepPRIME XD

Increasing the sensitivity a little to ISO 3200 with a shot from our Fujifilm X-S10 review, we have a tightly-cropped shot of a duck's head with the feathers freshy wetted from a visit beneath the water. Again, we can see quite a bit of noise prior to denoising, especially noticeable in the duck's eye. False color also intrudes, especially in the reflective water droplets and the fine folds of the duck's lower eyelid.

Switching to HQ denoising improves both of these defects considerably, but it definitely discards a good bit of fine detail too, throwing the baby out with the bathwater. You can see this most easily in the really fine feathers beneath and immediately behind the eye.

The DeepPRIME algorithm restores much of the detail in these areas, but seen 1:1 it also gives a somewhat unnatural, waxy appearance. It's noticeable in the feathers to some extent, but especially in the eyeball, which almost appears as if made of glass.

Switching to DeepPRIME XD brings back just a little more detail in the eye and its reflections. It also gives more crispness to the feathers. Again, this result is clearly the best of the group, especially bearing in mind it's at default settings with no user intervention.

Test 3: Samurai (Fujifilm X-H2S, ISO 5000)

No denoising	HQ denoising	DeepPRIME	DeepPRIME XD

Next up, we have an ISO 5000 shot of a samurai warrior figurine from our Fujifilm X-H2S review. With no denoising the glossy, black panels look quite speckled. And again, there's a little false color artifacting, which is especially noticeable in the shiny area along the upper edge of the small cord clip which you can see near the top center of the crop.

The HQ denoising algorithm cleans both of these up quite nicely, but it also robs us of the majority of the thread patterns in the fabric at the right of the crop. The threads in the cords are also largely lost, while some unnatural artifacts can be seen in the shiny bronze trim piece at the center of the crop.

The standard DeepPRIME algorithm does a noticeably better job with these defects, but it still looks a little soft when seen 1:1. Once again, DeepPRIME XD looks noticeably crisper, with more of the threads remaining visible in both fabric and cords.

Test 4: Dog (Fujifilm X-H2, ISO 6400)

No denoising	HQ denoising	DeepPRIME	DeepPRIME XD

Our next shot is at ISO 6400 and comes from our Fujifilm X-H2 review. With denoising disabled, it's quite a snowy affair, especially with the reflections in the dog's eyeball. Much of the detail in its fur quite heavily obscured by noise, especially in the blue channel. And while the HQ denoising filter helps, it still leaves things quite desaturated.

With the DeepPRIME filter active, not only is that noise mostly squashed, but the colors in the fur and the reflections in the dog's eye are noticeably more vivid. And that's even more true of the DeepPRIME XD version. The XD algorithm also rids the fur of some very fine artifacts that are especially noticeable in the top right corner of the standard DeepPRIME version.

Test 5: Street (Fujifilm X-T5, ISO 12800)

No denoising	HQ denoising	DeepPRIME	DeepPRIME XD

Thus far, all of these comparisons have played to the strengths of both DeepPRIME and DeepPRIME XD. As we've seen in our past coverage of these technologies – our DxO PhotoLab 6 review, for example – DxO's AI denoising tends to perform best when presented with naturally-occurring detail. Things like feathers, fur and foliage are its strengths.

Where it can sometimes stumble is with man-made detail, and especially text. So for this ISO 12800 shot from our Fujifilm X-T5 review, we've saved the hardest task for last. A nighttime city street scene, it's not only shot at the highest sensitivity of the bunch but also contains generous quantities of both of these AI bugbears.

As we can see above, DeepPRIME XD continues its winning ways with the ornate moldings at the bottom right of the crop, where your eye is fooled by the crispness and extra detail, and wouldn't necessarily notice any inaccuracies in the rendering. But these tricks don't work so well with the shop sign. (And the same holds true of the red advertising hoarding below, if you click through to the full-sized version.)

In both of those areas, the standard DeepPRIME algorithm – while noticeably less sharp than the XD variant – yields more readable-looking text with fewer unsightly artifacts. As humans, we're programmed to identify things like faces and writing: we can make these out relatively easily even if a bit blurred, but we also notice defects in their rendering more readily.

No denoising	HQ denoising	DeepPRIME	DeepPRIME XD

We're going to look at a second crop of this image, though, to point out some differences elsewhere within the frame. Here, we're looking a bit further to the right at the apartment windows, and there are a couple of things to note.

Firstly, DeepPRIME XD seems to be tuned to look for and attempt to replicate patterns more strongly than the standard DeepPRIME. That difference is especially visible in the window blinds; they're just barely visible in all but the DeepPRIME XD image, which renders them with much greater contrast.

Secondly, look at the roof tiles towards the bottom of the crop. In the DeepPRIME XD version, these have a somewhat unnatural, almost painterly look to them that isn't present in the DeepPRIME or other versions. This same effect is also noticeable in the background of the sign from the first crop, although there it's a little harder to tell what's going on.

Conclusion

From my testing, the arrival of DeepPRIME XD for X-Trans looks to be a win for Fujifilm X-series shooters, just as it is for those on other platforms. Although it isn't always going to be your best bet, it performs better than the alternatives frequently enough that if you've the time required for its processing, it's worth making your default option for high-ISO shots.

It's also worth noting that even when it struggles with things like text, you usually have to be looking at the image very closely to spot the defects. Viewed from a more typical distance, a DeepPRIME XD shot is going to create the impression of more detail and crispness, even if it's to some extent an artificial invention. When not pixel peeping, that can make the overall image feel better even when a more rigorous examination might tell a different story.

You have to bear your subjects, the viewer and how they'll be looking at your images in mind when deciding which algorithm to use.

The point here is that you have to bear your subjects, the viewer and how they'll be looking at your images in mind when deciding which algorithm to use. And we'll restate that we've only shown results at default settings; you can always dial back the strength of an algorithm to improve the results for any given shot.

Overall, I've found DeepPRIME XD to be a very useful tool and I'm thrilled that it's now available for X-Trans shooters too! If you're not already a PhotoLab customer, I highly recommend checking out the free 30-day trial with your own images to see if it's worth adding to your own digital darkroom.

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This image was captured with a Fujifilm X-T5 at ISO 12,800 and processed using DxO's DeepPRIME XD denoising algorithm.

It's been a little over a decade since Fujifilm launched its first camera with an X-Trans sensor, the Fujifilm X-Pro1, way back in January 2012. With a color filter array that samples red, green and blue pixels in every horizontal line and vertical column, X-Trans sensors are designed to more evenly sample the color information across the image. This, in turn, allows better resistance to color moiré versus the more traditional Bayer filter, which skips either the red or blue pixels in every other line or column.

The downsides of the X-Trans approach are twofold. With a larger 6x6 pattern rather than the 2x2 of Bayer, it makes the task of demosaicing the color data not just unique to Fujifilm cameras – requiring software makers to implement more custom code – but also more processor-intensive and challenging to optimize. And French software maker DxO's equally unique approach of denoising prior to demosaicing further complicated the situation. So for years it simply eschewed X-Trans support altogether, offering Fujifilm camera support only for its Bayer-based models.

The denoising samples throughout this article were made from these five images, shown here in their out-of-camera JPEG versions. You can click each crop in the rollover galleries below to open the full-sized, DxO-processed JPEG.

In October 2021, that finally started to change with the introduction of DxO's PhotoLab 5. That brought basic X-Trans support, although a couple of gaps remained in which noise reduction algorithms were available for it. The recent arrival of PhotoLab 6.4 has now closed the biggest of these, bringing support for the company's top-of-the-line DeepPRIME and DeepPRIME XD denoising algorithms to Fujifilm X-series shooters. (This leaves just the company's standard PRIME denoising algorithm and a handful of more minor features off the table for your X-series camera.)

So how does DeepPRIME get on with X-Trans imagery in the real world? Let's roll up our sleeves and take a closer look, courtesy of a selection of high ISO shots from our recent Fujifilm X-series reviews!

All sample images and their crops in this article were converted from raw using the just-released PhotoLab 6.5 update (also available for macOS) with its "DxO Optical Corrections only" preset, and each denoising algorithm was used at its default settings.

Test 1: Bread (Fujifilm X-T4, ISO 2500)

No denoising	HQ denoising	DeepPRIME	DeepPRIME XD

We'll start off with a shot from our Fujifilm X-T4 review at ISO 2500, a moderately high sensitivity. As you can see, with no denoising applied the noise intrudes quite a bit and, despite the X-Trans color filter array, there's still a fair bit of false color artifacting on display too.

The base HQ denoising algorithm cleans things up considerably, but it also robs some detail from the finer flour particles stuck to the bread, and the cracking in the crust looks a bit soft at 1:1 resolution.

And while DeepPRIME crispens things up, the newly-available DeepPRIME XD algorithm definitely does a much better job. Its result is even crisper, and it restores more of the fine details, yielding a less plasticky feel than the standard DeepPRIME.

Test 2: Duck (Fujifilm X-S10, ISO 3200)

No denoising	HQ denoising	DeepPRIME	DeepPRIME XD

Increasing the sensitivity a little to ISO 3200 with a shot from our Fujifilm X-S10 review, we have a tightly-cropped shot of a duck's head with the feathers freshy wetted from a visit beneath the water. Again, we can see quite a bit of noise prior to denoising, especially noticeable in the duck's eye. False color also intrudes, especially in the reflective water droplets and the fine folds of the duck's lower eyelid.

Switching to HQ denoising improves both of these defects considerably, but it definitely discards a good bit of fine detail too, throwing the baby out with the bathwater. You can see this most easily in the really fine feathers beneath and immediately behind the eye.

The DeepPRIME algorithm restores much of the detail in these areas, but seen 1:1 it also gives a somewhat unnatural, waxy appearance. It's noticeable in the feathers to some extent, but especially in the eyeball, which almost appears as if made of glass.

Switching to DeepPRIME XD brings back just a little more detail in the eye and its reflections. It also gives more crispness to the feathers. Again, this result is clearly the best of the group, especially bearing in mind it's at default settings with no user intervention.

Test 3: Samurai (Fujifilm X-H2S, ISO 5000)

No denoising	HQ denoising	DeepPRIME	DeepPRIME XD

Next up, we have an ISO 5000 shot of a samurai warrior figurine from our Fujifilm X-H2S review. With no denoising the glossy, black panels look quite speckled. And again, there's a little false color artifacting, which is especially noticeable in the shiny area along the upper edge of the small cord clip which you can see near the top center of the crop.

The HQ denoising algorithm cleans both of these up quite nicely, but it also robs us of the majority of the thread patterns in the fabric at the right of the crop. The threads in the cords are also largely lost, while some unnatural artifacts can be seen in the shiny bronze trim piece at the center of the crop.

The standard DeepPRIME algorithm does a noticeably better job with these defects, but it still looks a little soft when seen 1:1. Once again, DeepPRIME XD looks noticeably crisper, with more of the threads remaining visible in both fabric and cords.

Test 4: Dog (Fujifilm X-H2, ISO 6400)

No denoising	HQ denoising	DeepPRIME	DeepPRIME XD

Our next shot is at ISO 6400 and comes from our Fujifilm X-H2 review. With denoising disabled, it's quite a snowy affair, especially with the reflections in the dog's eyeball. Much of the detail in its fur quite heavily obscured by noise, especially in the blue channel. And while the HQ denoising filter helps, it still leaves things quite desaturated.

With the DeepPRIME filter active, not only is that noise mostly squashed, but the colors in the fur and the reflections in the dog's eye are noticeably more vivid. And that's even more true of the DeepPRIME XD version. The XD algorithm also rids the fur of some very fine artifacts that are especially noticeable in the top right corner of the standard DeepPRIME version.

Test 5: Street (Fujifilm X-T5, ISO 12800)

No denoising	HQ denoising	DeepPRIME	DeepPRIME XD

Thus far, all of these comparisons have played to the strengths of both DeepPRIME and DeepPRIME XD. As we've seen in our past coverage of these technologies – our DxO PhotoLab 6 review, for example – DxO's AI denoising tends to perform best when presented with naturally-occurring detail. Things like feathers, fur and foliage are its strengths.

Where it can sometimes stumble is with man-made detail, and especially text. So for this ISO 12800 shot from our Fujifilm X-T5 review, we've saved the hardest task for last. A nighttime city street scene, it's not only shot at the highest sensitivity of the bunch but also contains generous quantities of both of these AI bugbears.

As we can see above, DeepPRIME XD continues its winning ways with the ornate moldings at the bottom right of the crop, where your eye is fooled by the crispness and extra detail, and wouldn't necessarily notice any inaccuracies in the rendering. But these tricks don't work so well with the shop sign. (And the same holds true of the red advertising hoarding below, if you click through to the full-sized version.)

In both of those areas, the standard DeepPRIME algorithm – while noticeably less sharp than the XD variant – yields more readable-looking text with fewer unsightly artifacts. As humans, we're programmed to identify things like faces and writing: we can make these out relatively easily even if a bit blurred, but we also notice defects in their rendering more readily.

No denoising	HQ denoising	DeepPRIME	DeepPRIME XD

We're going to look at a second crop of this image, though, to point out some differences elsewhere within the frame. Here, we're looking a bit further to the right at the apartment windows, and there are a couple of things to note.

Firstly, DeepPRIME XD seems to be tuned to look for and attempt to replicate patterns more strongly than the standard DeepPRIME. That difference is especially visible in the window blinds; they're just barely visible in all but the DeepPRIME XD image, which renders them with much greater contrast.

Secondly, look at the roof tiles towards the bottom of the crop. In the DeepPRIME XD version, these have a somewhat unnatural, almost painterly look to them that isn't present in the DeepPRIME or other versions. This same effect is also noticeable in the background of the sign from the first crop, although there it's a little harder to tell what's going on.

Conclusion

From my testing, the arrival of DeepPRIME XD for X-Trans looks to be a win for Fujifilm X-series shooters, just as it is for those on other platforms. Although it isn't always going to be your best bet, it performs better than the alternatives frequently enough that if you've the time required for its processing, it's worth making your default option for high-ISO shots.

It's also worth noting that even when it struggles with things like text, you usually have to be looking at the image very closely to spot the defects. Viewed from a more typical distance, a DeepPRIME XD shot is going to create the impression of more detail and crispness, even if it's to some extent an artificial invention. When not pixel peeping, that can make the overall image feel better even when a more rigorous examination might tell a different story.

You have to bear your subjects, the viewer and how they'll be looking at your images in mind when deciding which algorithm to use.

The point here is that you have to bear your subjects, the viewer and how they'll be looking at your images in mind when deciding which algorithm to use. And we'll restate that we've only shown results at default settings; you can always dial back the strength of an algorithm to improve the results for any given shot.

Overall, I've found DeepPRIME XD to be a very useful tool and I'm thrilled that it's now available for X-Trans shooters too! If you're not already a PhotoLab customer, I highly recommend checking out the free 30-day trial with your own images to see if it's worth adding to your own digital darkroom.

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The R10 is for the weekend traveler who wants a throw-in-the-bag and it-just-works camera, the family gatherings documentarian who wants to be ready for the video of their kids playing in the park as well as the family portrait with the grandparents, the vlogger who wants to create selfies and videos at arms length, and the beginner who wants to start learning with manual controls. Read our full in-depth review of the camera.

With its AF smarts and affordable price we found it to be a great bang-for-your-buck camera, but how well does it perform in the field? We carried it on the streets of Seattle over nearly a year for a long-term test.

View the Canon R10 sample gallery

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