A feature-level full-reference image denoising quality assessment method based on joint sparse representation

摘要

This paper proposes feature-level full-reference image denoising quality metrics based on a joint sparse representation model. By decomposing a denoised image and its clean reference jointly and sparsely with a specific learning dictionary, our method measures the denoising quality from two contradictory perspectives, i.e., the detail preservation capability and noise suppression capability, which determine the denoising quality together, in an image feature space. This novel multiperspective method can not only measure the performance of denoising algorithms accurately but also provide a unique means for investigating denoising characteristics in a learning feature space. In the experiments, nine representative denoising methods and six widely used full-reference objective metrics were employed to verify the effectiveness of our method. In addition, the denoising influences exerted on dictionary atoms are investigated in depth, and several statistical conclusions are reported. Furthermore, our work also provides a new feasible assessment framework for other image recovery and generation tasks.