Patch-Based Nonlocal Adaptive Gradient Regularization for Image Restoration in Sensor Networks

In this paper, we propose a novel patch-based adaptive nonlocal gradient regularization method for image restoration in sensor networks. It formulates the hyper-Laplacian distribution to regularize the global gradient distribution. The patch-based nonlocal gradient prior is utilized to regularize the nonlocal self-similarity of image gradients. Firstly, the L0-norm smoothing scheme is used innovatively as the preprocessing step to preserve strong edges, which are critical to improve the accuracy of clustering the similar image patches. Then, adaptive weights for each patches are developed from a set of clustered nonlocal self-similarity patches by learning the the expectation and variance for sparse gradient distribution at each pixel. Comparing with several recent state-of-the-art methods, experimental results show that the proposed method has better performance in alleviating block effects and preserving image details.