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With the aim of retrieving high-quality images from corrupted versions, image restoration meets the extensive demand of application scenarios. State-of-the-art methods solve this problem by means of designing convolution blocks in multistage architectures. However, the existing methods blindly extract and fuse features without considering which parts of the features are more effective for image restoration. In this paper, we propose a significance-wise mechanism with the goal of identifying the importance degree of each region in the feature representation and providing crucial feature information. Through the calculation of nonlinear function in the significance-wise mechanism, the region which is more conducive to image restoration will get higher importance values. Two important types of information are generated within the attention mechanism, including: (i) feature-sufficient maps with abundant feature representations, and (ii) significance-wise maps with the importance degree of patch information in the corrupted images. With the coordination of feature-sufficient maps and significance-wise maps, the network architecture can focus higher attention on crucial parts of feature information. Furthermore, we design a multistage feature fusion block with the significance-wise mechanism. Compared with existing attention mechanisms, our significance-wise mechanism has the ability to identify and generate crucial feature representation for multiple image restoration tasks. Due to the novel design of the network, we successfully implement multiple restoration tasks only by fine-tuning the number of channels once on a network. Abundant quantitative and qualitative experiments demonstrate that the effective image restoration network (EIRN) outperforms existing state-of-the-art algorithms on eleven datasets across a series of image restoration tasks, including image deblurring, denoising, super-resolution, and deraining. The source code and pretrained models will be available at https://github.com/XinyueZhangqdu/EIRN. [ABSTRACT FROM AUTHOR]

As a classical secret sharing technique, (k , n) threshold secret image sharing mainly contains the shadow generation phase and the image reconstruction phase. The sensitive secret image is encrypted into n shadows by the dealer in the shadow generation phase, and the secret image can be reconstructed completely by the participants when the number of shadows meet or exceed the threshold k. Previous progressive or hierarchical secret image sharing schemes have some disadvantages (e.g., inflexibility and inefficiency). To solve this problem, we propose a progressive and hierarchical image segmentation-sharing scheme (referred to as PHISS) in this paper. First, the important content region of the sensitive image (called by the target sub-image) is generated by using the proposed image segmentation algorithm. Here the target sub-image refers to the part with the largest amount of information, for instance, the human face in private photo, the pathological region in medical image and the aircraft in military image. Then, in the shadow generation phase, the target sub-image is encrypted into several shadows with hierarchy, and the hierarchies of the generated shadows in the background sub-image are equal. Last, in the reconstruction phase, the background sub-image can be reconstructed completely. The target sub-image has different quality with different number of shadows and different level of shadows. The image can be reconstructed losslessly if and only if all shadows involved. The experimental results and the comparison with similar works demonstrate the feasibility and the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Total Variation (TV) regularization is a widely used convex but non-smooth regularizer in image restoration and reconstruction. Many algorithms involve solving a denoising problem as an intermediate step or in each iteration. Most existing solvers were proposed in the context of a specific application. In this paper, we propose a denoising method which can be used as a proximal mapping (denoising operator) for noises other than additive and Gaussian. We formulate the Maximum A-Posteriori (MAP) estimation in terms of a spatially adaptive and recursive filtering operation on the Maximum Likelihood (ML) estimate. The only dependence on the model is the ML estimate and the second order derivative, which are computed at the beginning and remain fixed throughout the iterative process. The proposed method generalizes the MAP estimation with a quadratic regularizer using an infinite impulse response filter, to the case with TV regularization. Due to the fact that TV is non-smooth and has spatial dependencies, the resulting filter after reweighted least squares formulation of the TV term, is recursive and spatially variant. The proposed method is an instance of the Majorization–Minimization (MM) algorithms, for which convergence conditions are defined and can be shown to be satisfied by the proposed method. The method can also be extended to image inpainting and higher order TV in an intuitively straight-forward manner. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose three new time dependent models based on total variation regularized model for solving total variation minimization problems in image restoration. The main idea is to apply a priori smoothness on the solution image and to regularize the parabolic term. We propose a kind of continuous boundary conditions, i.e. mean boundary conditions. Numerical experimental results demonstrate that the three new models need far less iterations than the original total variation model and mean boundary conditions are efficient for image restoration. [Copyright &y& Elsevier]