  Camera Image Signal Processing (ISP) pipelines can get appealing results in
different image signal processing tasks. Nonetheless, the majority of these
methods, including those employing an encoder-decoder deep architecture for the
task, typically utilize a uniform filter applied consistently across the entire
image. However, it is natural to view a camera image as heterogeneous, as the
color intensity and the artificial noise are distributed vastly differently,
even across the two-dimensional domain of a single image. Varied Moire ringing,
motion blur, color-bleaching, or lens-based projection distortions can all
potentially lead to a heterogeneous image artifact filtering problem. In this
paper, we present a specific patch-based, local subspace deep neural network
that improves Camera ISP to be robust to heterogeneous artifacts (especially
image denoising). We call our three-fold deep-trained model the Patch Subspace
Learning Autoencoder (PSL-AE). The PSL-AE model does not make assumptions
regarding uniform levels of image distortion. Instead, it first encodes patches
extracted from noisy a nd clean image pairs, with different artifact types or
distortion levels, by contrastive learning. Then, the patches of each image are
encoded into corresponding soft clusters within their suitable latent
sub-space, utilizing a prior mixture model. Furthermore, the decoders undergo
training in an unsupervised manner, specifically trained for the image patches
present in each cluster. The experiments highlight the adaptability and
efficacy through enhanced heterogeneous filtering, both from synthesized
artifacts but also realistic SIDD image pairs.
