![]() Solutions that were previously beyond most of our imaginations are now available for our daily use. Nowadays, there are hardly any days not to see some news about AI. In the last few years, the scientific community has made a great leap in the Deep Learning field, also known as Artificial Intelligence (AI) in public. Just like any photographer knows, when over-applied, noise removal algorithms are very notorious for removing details and making images very flat. This distinction is very important, as the other type of variation details are very precious to us. The main difficulties lay on identifying the variation we do allow and what we don’t. Overall, the main idea is to look around the neighboring pixels and only allow a certain type of variation. ![]() Therefore, many clever solutions were devised by many researchers over time to address different types of image noise. For example, most of us find chroma noise more irritating than the luminance noise. As a viewer, we perceive different noises in different ways. There is another aspect that makes the problem evenharder. ![]() To make things worse, the type and contribution vary in each capture and even in the different parts of the same image. The atmosphere, optics, sensors, and digital circuits all can contribute to the final capture in different degrees. Removing noise from images is considered a difficult problem because of the variety of noise sources. Why is image noise reduction such a challenge? Over the last decade, Topaz Labs has been dedicated to developing the best possible tools of that time to address this problem, along with other common editing hardships. Even for more traditional photography, we sometimes find noise in shadow areas when adjusted in post-editing. Using AI to deliver remarkable image noise reductionĪs photographers, we all have situations where we end up with noisy photos, like when we’re shooting in low lighting or shooting fast actions.
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