Regularization by denoising (RED) framework has shown impressive performance for many imaging inverse problems, by leveraging the denoising method in defining an explicit regularization. In this letter, we propose a novel SLN-RED scheme for image restoration by exploiting the local and nonlocal denoisers simultaneously. Theoretically, we proves that for bounded denoisers, the SLN-RED under ADMM scheme with a continuation strategy converges to a fixed-point. Numerical experiments on deblurring and super-resolution tasks demonstrate promising performance of the proposed algorithm.

In this paper, we propose a simple but effective illumination distribution prior (IDP) for images to illuminate the darkness. The illumination distribution prior is the product of a statistical approach to low-light images. It is based on a key factor - the mean value and standard deviation of images are positively correlated with the illumination. Using IDP in combination with the dual-domain feature fusion network (DFFN), we can obtain images that are more consistent with the ground truth distribution. DFFN inserts the discrete wavelet transform (DWT) into the transformer architecture, aiming to recover the detailed texture of the image through local high-frequency information and global spatial information. We have conducted extensive experiments on five widely used low-light image enhancement datasets and the experimental results show the superior performance of our proposed network (IDP-Net) compared to other state-of-the-art methods.

We present a new improved parsimonious multivariate Markov chain model. Moreover, we find a new convergence condition with a new variability to improve the prediction accuracy and minimize the scale of the convergence condition. Numerical experiments illustrate that the new improved parsimonious multivariate Markov chain model with the new convergence condition of the new variability performs better than the improved parsimonious multivariate Markov chain model in prediction.

Wavelet tight frames have been actively investigated for various image restoration problems. In this paper, we introduce an analysis-sparsity model via $\ell _2$ -relaxed truncated $\ell _0$ regularization and nonlocal estimation, and the resulted nonconvex minimization problem is tackled by a proximal alternating minimization strategy. Numerical experiments demonstrate that the proposed algorithm is superior to many popular methods in both objective and perceptual quality.