In this talk, we focus on image recovery under deep learning and dictionary learning. For deep learning, we focus on image recovery under Cauchy noise. Instead of the celebrated total variation or low-rank prior, we adopt a novel deep-learning-based image denoiser prior to effectively remove Cauchy noise with blur. To preserve more detailed texture and better balance between the receptive field size and the computational cost, we apply the multi-level wavelet convolutional neural network (MWCNN) to train this denoiser. We use the forward-backward splitting (FBS) method to handle the proposed model, which can be implemented efficiently without introducing auxiliary variables. Moreover, the multi-noise-levels strategy is employed to train a series of denoisers to restore the image corrupted by Cauchy noise and blur. For dictionary learning, our recently developed models are to use the total variation regularization to represent sharp edges in color images, and the pure quaternion constraint to force sparse representations of color images containing only red, green, and blue color information. The proposed model can be solved by the alternating minimization method (AMM). Experimental results are reported to demonstrate that the proposed models can provide better denoising results than the existing methods in terms of PSNR, SSIM, and visual quality.

会议ID:919 764 396