![]() ![]() ![]() With these updates, color blotchiness and patterns are significantly reduced, especially in the highlight and shadow regions. ![]() That’s because we added a smaller model with the sole purpose of matching the input and output color, thereby establishing improved color consistency of the image after the model processes it. This new approach allows the model to focus on improving noise reduction without worrying about color consistency… yet. To alleviate that issue, we adjusted our approach and reduced that color consistency constraint (say that three times fast □) to work on larger regions of the image as opposed to at a very local level. This was due to the Low Light model preserving some of the color in the noise pattern of the original at a very local level, in an effort to provide more consistent color. When using the Low Light v3 model in DeNoise v3.6, you may have experienced blotchiness, or patterns, especially in the highlight and shadow regions of very noisy photos. Improved color consistency with Low Light v4 Bug fix highlights include plugging up a memory leak when batch processing using the RAW model, improved handling of batch import into Adobe Lightroom Classic, and a new workflow that prevents crashes when applying the RAW model to Canon CR2 files.įor a limited time, DeNoise AI is $20 off and you can get an additional 15% off with promo code DENOISE15 (expires July 8, 2022).Īdditional information on the latest Topaz Labs DeNoise AI version 3.7: Important stability improvements and bug fixes – Additional camera model RAW files are now supported, including the highly requested Olympus OM1.Updated TensorRT models – Users of supported NVIDIA GPUs will experience performance improvements, especially when using the RAW model.Improved color consistency with Low Light v4 – Improvements to how we trained Low Light v4 allows us to provide more consistent color while reducing blotchiness in the highlights and shadows.Topaz Labs released DeNoise AI version 3.7 which introduces improved color consistency with Low Light v4, updated TensorRT models, important stability improvements, and bug fixes: ![]()
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