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Found 4 papers, 3 papers with code
On the Importance of Denoising when Learning to Compress Images
Image noise is ubiquitous in photography.
End-to-end optimized image compression with competition of prior distributions
Convolutional autoencoders are now at the forefront of image compression research.
Adapting JPEG XS gains and priorities to tasks and contents
Most current research in the domain of image compression focuses solely on achieving state of the art compression ratio, but that is not always usable in today's workflow due to the constraints on computing resources.
Natural Image Noise Dataset
We introduce the Natural Image Noise Dataset (NIND), a dataset of DSLR-like images with varying levels of ISO noise which is large enough to train models for blind denoising over a wide range of noise.
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