Deep Semantic Statistics Matching (D2SM) Denoising Network
This addresses the problem of image restoration for computer vision applications by improving denoising in a way that benefits high-level tasks, though it appears incremental as it builds on existing pretrained networks.
The paper tackles image denoising by introducing a method that matches semantic statistics to preserve image correlations, resulting in improved denoising performance and enhanced semantic segmentation accuracy on the Cityscapes dataset.
The ultimate aim of image restoration like denoising is to find an exact correlation between the noisy and clear image domains. But the optimization of end-to-end denoising learning like pixel-wise losses is performed in a sample-to-sample manner, which ignores the intrinsic correlation of images, especially semantics. In this paper, we introduce the Deep Semantic Statistics Matching (D2SM) Denoising Network. It exploits semantic features of pretrained classification networks, then it implicitly matches the probabilistic distribution of clear images at the semantic feature space. By learning to preserve the semantic distribution of denoised images, we empirically find our method significantly improves the denoising capabilities of networks, and the denoised results can be better understood by high-level vision tasks. Comprehensive experiments conducted on the noisy Cityscapes dataset demonstrate the superiority of our method on both the denoising performance and semantic segmentation accuracy. Moreover, the performance improvement observed on our extended tasks including super-resolution and dehazing experiments shows its potentiality as a new general plug-and-play component.