CS-PCN: Context-Space Progressive Collaborative Network for Image
  Denoising
Yuqi Jiang Chune Zhang Jiao Liu
Currently, image-denoising methods based on deep learning cannot adequately
reconcile contextual semantic information and spatial details. To take these
information optimizations into consideration, in this paper, we propose a
Context-Space Progressive Collaborative Network (CS-PCN) for image denoising.
CS-PCN is a multi-stage hierarchical architecture composed of a context mining
siamese sub-network (CM2S) and a space synthesis sub-network (3S). CM2S aims at
extracting rich multi-scale contextual information by sequentially connecting
multi-layer feature processors (MLFP) for semantic information pre-processing,
attention encoder-decoders (AED) for multi-scale information, and multi-conv
attention controllers (MCAC) for supervised feature fusion. 3S parallels MLFP
and a single-scale cascading block to learn image details, which not only
maintains the contextual information but also emphasizes the complementary
spatial ones. Experimental results show that CS-PCN achieves significant
performance improvement in synthetic and real-world noise removal.