Enhanced Latent Space Blind Model for Real Image Denoising via Alternative Optimization

Motivated by the achievements in model-based methods and the advances in deep networks, we propose a novel enhanced latent space blind model based deep unfolding network, namely ScaoedNet, for complex real image denoising. It is derived by introducing latent space, noise information, and guidance constraint into the denoising cost function. A self-correction alternative optimization algorithm is proposed to split the novel cost function into three alternative subproblems, i.e., guidance representation (GR), degradation estimation (DE) and reconstruction (RE) subproblems. Finally, we implement the optimization process by a deep unfolding network consisting of GR, DE and RE networks. For higher performance of the DE network, a novel parameter-free noise feature adaptive enhancement (NFAE) layer is proposed. To synchronously and dynamically realize internal-external feature information mining in the RE network, a novel feature multi-modulation attention (FM2A) module is proposed. Our approach thereby leverages the advantages of deep learning, while also benefiting from the principled denoising provided by the classical model-based formulation. To the best of our knowledge, our enhanced latent space blind model, optimization scheme, NFAE and FM2A have not been reported in the previous literature. Experimental results show the promising performance of ScaoedNet on real image denoising. Code is available at https://github.com/chaoren88/ScaoedNet.