This is not a valid comparison of the many complex packages reviewed here! No one every slams the sliders all the way to the right as the basis for testing software. Noise removal needs subtle adjustments of the various setting in any package. Also AI is not all about 'filling in the details'. In these apps, AI can help adjust the various algorithms to suit the type of object from which it is removing noise.
DxO shines when you shoot RAW with a camera and lense that they measured. The algorithm is then informed of how much noise to expect from your sensor for each color, and it'll denoise the sensor data, before noise is spread to surrounding pixels (when the color image is produced from the sensor).
Your sensor is analog and colorblind - it has no data, and it has no pixels. What the likes of DxO call "noise" you may attribute to the sensor, and I would be equally justified to attribute it to raw processing. Elsewhere, I got corrected that DxO Mark and DxO of PhotoLab are different legal entities. My guess is that the DxO Mark company tests cameras and lenses, and sells the results to DxO, Adobe, and other licensees. As raw processing - turning raw photosite data into pixels - is a mathematically precise and repeatable form of wild-assed guessing for missing colors, what you see in post on your monitor display is the result of that guessing. You don't know what is actual "sensor noise", "camera noise" or "raw processing error". In that sense, comparing two different digital cameras in Lightroom is much more reviewing Lr's ability to turn a raw files into pixels for the respective cameras than it is to review these cameras. When raw processing, visibly recognizably to us, falters we might call that "Moiré" but when we do not recognize it, we call it noise and blame the camera. And that, learned friend, is a load of you know what. To what extent are our raw file's qualities determined by the "camera" rather than the "sensor"? Well, the camera scans (i.e. measures) the photosites in the sensor and the sensor may be bundled (a.k.a. stacked) with an Analog to Digital (AD) conversion circuit - which runs on an algorithm of the camera manufacturer. This is where you have your 14 bits and monochrome data. Why monochrome? Because the sensor has a filter-grid overlay with red (R), green (G) and blue (B) filters - one filter over one photosite. This gives "monochrome" photosites next to each other. When the filter grid is according to Mr. Bayer's ideas, then it has a 2x2 R,G,G,B pattern for every 2x2 photosites. This filter grid may be complemented with another filter that I call the fuzzy filter. This disperses light traveling to photosite [x,y] to the neighbors of that photosite too - as a hardware solution to make raw processing easier. By dispersion - i.e. by adding fuzziness. And this idea of hardware AA was already implemented in scanning tunneling electron microscopes in the 1970s to make software rendering easier. If you have a rather monotonous surface with an occasional speck of color in it, raw processing may well generate a form of noise called Moiré elsewhere. I have seen it from Lr and did not see it in the same images from DxO PhotoLab 4, nor C1, nor Nikon NX Studio (predecessor). Imagine, for subject, you have a black area next to a white area and in each there is a tiny red dot that has the size of a single photosite in the camera. It may be over a blue filtered photosite in the black half of the frame and over a green filtered photosite in the white half of the frame. In brightly lit circumstances. I would call something noise when it comes from the uncertainty range of measuring a photosite where it is hard to decide if any light is falling on a photosite, and if so, precisely how much. In my example that is not the case. Still, you will see "color noise", not from the camera, but from raw processing that has no clue about how the dot should be red. A camera with a fuzzy filter may help to recover the red to some extent, my camera does not have an AA filter ...
@@jpdj2715 but you do get the point that if you're going to guess your SNR and apply it to a noise reduction algorithm, you're better off doing it before converting RAW to RGB? Once you're in RGB, you've already spread a noisy photosight to the neighboring pixels. My expertise is in audio (quantization, coding, noise reduction, etc) but similar concepts apply here.
@@j777 - one of my points was that what you see on your PC display and call noise in many cases is the result of raw processing and no noise at all. With your kind of deep expertise in digital audio, you should understand the difference. Imagine you have a 70 dB signal and someone tries to convince you that it is noise (as opposed to having underlying noise)? I would not talk about S/N ratio in terms of "guessing" it. As you understand coding, and no doubt decoding, note that in raw processing, the first step is deBayerization - extrapolation guessing of missing colors by interpolation with neighboring photosite data. This leads to a slowly damped (color) resonance that we call Moiré when we recognize it. To take care of that cross bleeding of colors from one blob into the neighboring and vice versa, the next raw step is "demosaicking" that should remove things like Moiré. Here we get to the point that the algorithm must do image analysis to decide where edges of blobs are (e.g. with the convolution algorithm of around 1990 in computer vision AI). If you look at old JPEGs (the result of raw processing), you'll see cross bleeding all over the place. In recent years this is much better. Moore's Law paired with better software. Here we - armchair architects of raw processing - have to wonder if adding context knowledge (AI) can further our cause. And, potentially, shifting demosaicking into deBayerization can prevent problems rather than creating things that need to be solved later. IMO this all has nothing to do with "noise" or "color noise". You can basically compare each photosite in your sensor with a thermo-couple on a Fluke or Omega multi-port logging thermometer. Better shielding, better bi-metal, etc. lead to better S/N ratio and slightly more precise results. If you want to compare with other electronic fields, the precision at each photosite is really good and we get 14 bits gradation resolution from cameras measuring them. The uncertainty about "noise" at the moment you snap your pic is in the AD circuit of the camera that converts the analog photosite values into a digital integer. I would argue that uncertainty about "noise" is in the bottom end of the integer number range coming out of AD. The problem is missing colors. You don't have that problem in audio or with a digital thermometer. Nor do you have to pay much attention to the problem of aliasing. An audio engineer (BSc/MSc from a top tier research university) once informed me that he could design and build a good amplifier with Ohm's law and a drawer cabinet of components. He did not "understand" bandwidth and wave-shape. How do you hear the difference between a piano, a flute and a clarinet all playing the piano's central A note of 440 Hz? Because of difference in wave-shape and this requires much, much more bandwidth than a 440Hz sine wave, especially when they play simultaneously and you can still pinpoint them in space. As frequency dependent phase shifts alter wave shape, the clarinet easily becomes a saxophone in your ears. Such differences are easily lost in digital audio, especially at playback. In digital photography we miss a lot and you can write a simple algorithm to fill in a gap in a digital audio stream, but at some point "we" identify it and don't like it. Apologies for the aside. I would say that 9 out of 10 cases of "color noise" are actually failed raw conversions - nothing to do with "noise". So if DxO sell PureRAW that works your raw file into an improved raw file that processes better in Lr, then what does it actually do? Give Lr a file that needs less demosaicking after its naive deBayerization?
DXO PhotoLab’s results when given a high-ISO RAW file are nothing short of remarkable. You spent about 30 seconds moving one slider in the most minimal version of noise reduction the offer, and on a JPG file. This wasn’t a very in-depth test.
Lightroom option is the same as the Adobe Camera Raw engine, accessible through the PS filters tab or directly from Bridge. (obviously works better on RAW images)
I don’t comment enough, but I want to say once again how helpful your videos are when I am trying to get some things worked out. I am not very good at experimenting to see what different things do. You do a great job of both showing how to do things, but also explaining what you are using and why. This helps me figure some things out for myself, because it is easier to apply knowledge when it is actually explained and understood.
Thanks Aaron! The new version of topaz denoise ai with severe noise is amazing. Better in my eyes than dxo pureraw / photolab 4 / deep prime. I use all these commercial programmes to test which one works best on the images i edit for my clients. Denoise ai seems to be the best out there.
@@sarimner yes true, but you can't export as RAW only as DNG. And the file size goes from 30mb to almost 200mb! This is fine if you are working on a single image. But as a wedding photographer working with hundreds of images this is not workable.
I use Topaz DeNoise AI AND Neat Image 8.6. I like Neat as you analyze the specific noise on the image and have a lot of tuning abilities. I occasionally run the image through both tools!
@@Mappi75 actually no. It depends upon the image. I do a lot of restorative work. The noise in scans of old photograph varies quite a bit. When I am cleaning up noise from a high ISO picture, DeNoise is hard to beat. But noise from a dirty or textured photo, requires neat image. If it is a grainy, dirty, textured image, then I will play around with combinations of both tools on multiple layers then choose the best.
I use Neat in Premiere Pro and like it although just like the other noise reducers it can leave skin looking waxy if you go too far. But at the end of the video Aaron asked you to make suggestions as to what you use so I wouldn't call that ignoring the elephant.
Since watching and commenting originally, I tried and returned an A7iv, which enlightened me as to how good the new cameras are. Sine my upgrade path from my D7200 would involve all new glass, lighting equipment etc., i got the Topaz suite instead. With it, I find Gigapixel Ai, even without enlarging, drastically reduces Noise, improves Shapness, Facial Clariy, Eyes and Eyelashes and even any CA since it's sharpening tightens up the edges so much. I find it mind boggling and other than for video, which I just use my s21ultra phone for, I can live with what I have for the foreseeable future.
You should try DeepPRIME instead of HQ in DxO software. (RAW only) DeepPRIME is one of the best noise reduction features. It is simple, easy to use, and provides more details and more natural looks than Topaz DeNoise AI. HQ is totally rubbish. Topaz often produces strange textures or erases excessive details after noise reduction, but the advantage is that it is not limited to the raw file. (comparing to the DeepPRIME)
Thanks for the comparison on Vance AI. Apart from noise reduction it is amazing for image upscaling using AI. I did previously use Remini - an iPhone app only, which meant I had to roundtrip with an iPhone.
Guys this was a poor standard for your usual quality. To not use DXO Deep prime means this check was useless as Deep Prime is awesome and didn't think you were fair with Denoise AI either. If you are going to do something like this then at lease do it accurately and fairly. Love your stuff guys so I am not throwing shade on the channel, just this wasn't your best or even fair.
Hi Aaron, I have been using a plug in of Imagenomics Noiseware for many years. love that software. Also, use Topaz at times. this I started to use fairly recently.
I have been using Topaz for a good while now and really like it. I shoot high school sports in mixed lighting and it really does a nice job targeting the noise versus just smoothing all the pixels.
The AI programs mostly do some kind of sharpening also. Is there a reason that you didn't also do some sharpening in LR/ACR? Especially with the masking function on, it can improve the results a bit.
Great review, Aaron! Exactly what we need right now as there are so many options, thanks to the preponderance of AI... Will you be comparing On1 noise reduction when it releases?
Thanks for the comparisons. I just use the now near excellent NR in LR, together with secondary sharpening using a LR brush preset in which I have strong texture and contrast with which I can add detail where it is needed. I have Luminar ai as well but find it clumsy and ineffective compared to LR for this type of workflow. LR's brush tool is phenomenal IMHO.
@@AndyDay It is getting better. Since then I tried the A7iv and learned how clean the new cameras are as compared to my D7200. So, I got the Topaz suite, and run my higher iso shots and soft focus shots through that. Mostly Gigapixel Ai. Often I don't even enlarge since 24mp is plenty unless severely cropped and intending to print. Time marches on and these softwares reduce the need for spending 10 grand to move to the Z system or to Sony and have to replace all my lenses and lighting.
Thanks, a very useful video. Everyone has a once-in-a-lifetime shot, perfect but for the noise. Aren't there advanced methods in Photoshop that can reduce noise better than any o these apps?
For those looking for a free solution, Photo-toolbox.com has a free photo noise reduction module you can download. It has separate slider sections for luminosity noise and color noise reduction. It supports a large number of file formats, including many RAW formats.
Topaz came out of that looking really good imo. Is DxO the bundle that isn't being updated anymore? It looks like old tech. I'm amazed that Adobe doesn't make something better for their programs. One would think that it's something one would want to make part of a PHOTO shop?
Now Photolab uses the older Prime technology that they already had in DxO Optics Pro from years ago ;) But now they added DeepPrime AI and this is fantastic. In PureRaw it is all automated but in PhotoLab you can adjust more so you have more control. PureRAW sometimes makes the image to sharp. DxO Nik Collection i used in the passed too when is was free. But i then preferred the Prime denoise from Dxo Optics Pro. Also tried lately Topaz Denoise AI 3.2 but i did not get that good results as with DxO on RAW files, also when it makes the DNG fille it changes the colors, you can offcourse repair it but it takes a bit more time.
Aaron, you had the slider for "Recover Original Detail" in Topaz set to "0". Would sliding that higher have changed the oil painting AI into more realistic rendition? Oh, you used JPEG and that's a pity. DeepPRIME in DxO PhotoLab 4 is great and it would have been nice to compare - DxO have taken this deep stuff out into a separate app PureRAW and this turns your raw file into an improved - hopefully - raw file which preserves your Lr-Ps workflow.
I use Neat in Premiere Pro and like it although just like the other noise reducers it can leave skin looking waxy if you go too far. (in reply to another viewer) But at the end of the video Aaron asked you to make suggestions as to what you use so I wouldn't call that ignoring the elephant.
@@airb1976 not an even playing field in my opinion, as a foot note though, I own topaz denoise I don't own dxo pure raw , dxo is however the best out there having used their trial version it's superior to topaz atm in my opinion
I love your work Aaron and you are a truly great educator, but you really don’t need to say you are going to ‘go ahead and’ adjust something every single time you use your mouse. It’s a youtubeism that’s becoming more and more common and it’s going to drive me insane.
An inadequate review because you are using a jpeg . Also you don’t not mention that Dxo doesn’t handle Fuji files . First time I’ve ever given you a thumbs down
Great comparison, and I really enjoy your channel overall. But this would have been way more helpful with a better original file. The one you used falls squarely within the ambit of "totally unusable." It was a JPG with more noise than anyone is entitled to. In my opinion, all six programs rendered awful results. But that's only because there are no algorithms that could have reduced that much noise without destroying detail, and AI reconstruction is really just a computer making an educated guess. Great for predicting my shopping habits, but not so great for reconstructing an ear after blurring it to oblivion. I'm curious how these products perform with a better original file, and in raw.
