Learning to See in the Dark (2018)
Hi, I&#x27;m a developer at NexOptic[0] and we are a company that was deeply inspired by this paper when it was first published. We had a lot of early success when attempting to replicate the results on our own and ended up running with it, and extending it into our own product line under our ALIIS brand of AI powered solutions.<p>For those curious, our current approach differs in some very significant ways to the author&#x27;s implementation, such as performing our denoising and enhancement on a raw bayer -&gt; raw bayer basis with a separate pipeline for tone mapping, white-balance, and HDR enhancement. As well, we explored a fair amount of different architectures for the CNN and came to the conclusion that a heavily mixed multi-resolution layering solution produces superior results.<p>As other commentators have pointed out, the most interesting part of it is really coming to terms that, as war1025 pointed out, &quot;The message has an entropy limit, but the message isn&#x27;t the whole dataset.&quot; It is incredibly powerful what can be accomplished with even extraordinarily noisy information as long as one has a extremely &quot;knowledge packed&quot; prior.<p>If anyone has any questions about our research in this space, please feel free to ask.<p>[0] <a href="https:&#x2F;&#x2F;nexoptic.com&#x2F;artificialintelligence&#x2F;" rel="nofollow">https:&#x2F;&#x2F;nexoptic.com&#x2F;artificialintelligence&#x2F;</a>