Reinforcement Learning of Self Enhancing Camera Image and Signal Processing

TL;DR

This paper introduces RSE-RL, a reinforcement learning model that adaptively enhances camera images by recursively identifying and correcting spatially heterogeneous artifacts using patch-based processing and latent space clustering.

Contribution

The paper presents a novel recursive reinforcement learning framework that improves image quality through adaptive, patch-based artifact filtering in learned latent subspaces, addressing spatial heterogeneity.

Findings

Effective removal of heterogeneous noise and artifacts

Recursive training enhances robustness and image quality

Outperforms traditional uniform filtering methods

Abstract

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.