Specularity Factorization for Low-Light Enhancement

arXiv:2404.01998v1 Announce Type: cross
Abstract: We present a new additive image factorization technique that treats images to be composed of multiple latent specular components which can be simply estimated recursively by modulating the sparsity during decomposition. Our model-driven {\em RSFNet} estimates these factors by unrolling the optimization into network layers requiring only a few scalars to be learned. The resultant factors are interpretable by design and can be fused for different image enhancement tasks via a network or combined directly …

abstract arxiv components cs.cv cs.lg factorization image images light low multiple network optimization sparsity type