Noiseless AI in Luminar Neo did a decent job smoothing noise while retaining – indeed sharpening – ground detail without introducing ringing or colorful edge artifacts.Topaz Photo AI, despite being from the same company as DeNoise AI, did a poor job, producing lots of noisy artifacts in the sky and an over-sharpened foreground riddled with colorful speckling.Even so, it also produced some patchiness, with some areas showing more noise than others. Topaz DeNoise AI did a better job than NoNoise AI, retaining the sharp ground detail while smoothing noise, always more obvious in the sky in such images.It left a uniform pixel-level mosaic effect in the shadow areas. It also produced somewhat patchy noise smoothing in the sky. ON1 NoNoise 2023 retained landscape detail better than ACR but softened the star trails, despite my adding sharpening.Adobe Camera Raw’s basic noise reduction did a good job, but like all general routines it does soften the image as a by-product of smoothing out high-ISO noise.I shot the image of Lake Louise in Banff, Alberta with a Canon RF15-35mm lens on a 45-megapixel Canon R5 camera at ISO 1600. Of interest is how well each preserves ground detail.Īs with all test images here, the panels show a highly magnified section of the image, indicated in the inset. This compares the programs on a typical nightscape image, with the inset showing the small area blown up in each panel. A close-up deep-sky image taken with a telescope at a high ISO of 3200, showing thermal noise hot pixels.Įach is a single image, not a stack of multiple images.A wide-field deep-sky image at ISO 1600 with an 85mm lens, with very tiny stars.A nightscape with star trails and a detailed foreground, taken at ISO 1600. ![]() ![]() This shows the three test images, lightly processed in Adobe Camera Raw for color correction and contrast, before applying noise reduction with the various programs.Īs shown above, I chose three representative images: I tested PureRAW 2 by dropping raw Canon CR3 files into the app and then exporting the results as raw DNG files. It can work only on raw files as a stand-alone app, or via an Adobe Lightroom plug-in that exports files to it. While some of the programs can be used as stand-alone applications, I tested them all as plug-ins for Photoshop, applying each as a smart filter applied to a developed raw file brought into Photoshop as a Camera Raw smart object. Click on the product names to go to their websites for more information and pricing. The others are general purpose noise reduction programs. Noise XTerminator is made specifically for astrophotos. RC-Astro NoiseXTerminator Version tested: 1.1.2, AI model 2 Luminar Neo Noiseless AI Version tested: 1.5.0 ON1 NoNoise AI 2023 Version tested: 17.0.1 However, ACR’s routine (also found in Adobe Lightroom) has not changed in years. I use this as a base comparison, as it has been the noise reduction software I have long applied to images. In the test results for the three images below I show the original raw image plus a version with noise reduction and sharpening applied using Adobe Camera Raw’s (ACR) own sliders with luminance noise at 40, color noise at 25, and sharpening at 25. The benefit of AI-based noise reduction (at right) is that it can, in theory, remove noise more effectively than non-AI programs (at center) without harming details or sharpness. To find out, I tested six of the new AI-based programs on three sample astrophotos. How well can each program reduce noise without eliminating stars or wanted details, or introducing odd artifacts, making images worse? However, in astrophotography our main subjects – stars – can look a lot like specks of pixel-level noise. ![]() The new generation of software uses artificial intelligence (AI), aka machine learning, trained on thousands of images to better distinguish unwanted noise from desirable image content. ![]() In the last two years we have seen a spate of specialized programs introduced for removing digital noise from photos. How well do new AI noise reduction programs work for astrophotography? I tested six to find out.
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