  Current camera image and signal processing pipelines (ISPs), including
deep-trained versions, tend to apply a single filter that is uniformly applied
to the entire image. This is despite the fact that most acquired camera images
have spatially heterogeneous artifacts. This spatial heterogeneity manifests
itself across the image space as varied Moire ringing, motion-blur,
color-bleaching, or lens-based projection distortions. Moreover, combinations
of these image artifacts can be present in small or large pixel neighborhoods,
within an acquired image. Here, we present a deep reinforcement learning model
that works in learned latent subspaces, and recursively improves camera image
quality through a patch-based spatially adaptive artifact filtering and image
enhancement. Our \textit{Recursive Self Enhancement Reinforcement
Learning}(RSE-RL) model views the identification and correction of artifacts as
a recursive self-learning and self-improvement exercise and consists of two
major sub-modules: (i) The latent feature sub-space clustering/grouping
obtained through variational auto-encoders enabling rapid identification of the
correspondence and discrepancy between noisy and clean image patches. (ii) The
adaptive learned transformation is controlled by a soft actor-critic agent that
progressively filters and enhances the noisy patches using its closest feature
distance neighbors of clean patches. Artificial artifacts that may be
introduced in a patch-based ISP, are also removed through a reward-based
de-blocking recovery and image enhancement. We demonstrate the self-improvement
feature of our model by recursively training and testing on images, wherein the
enhanced images resulting from each epoch provide a natural data augmentation
and robustness to the RSE-RL training-filtering pipeline. Our method shows
advantage for heterogeneous noise and artifact removal.
