Recently, deep neural networks have been successfully applied for removing rain streaks from images. However, these methods did not consider the relationships between the skip layers. In order to improve the deraining performance and use the information of different layers, we propose to construct connections between different layers and propose a residual skip connection neural network for single image deraining. Then we train the network on synthesized data. Both experiments on synthesized and real-world images show the proposed method outperforms state-of-the-art methods in terms of rain streak removal and image information preservation.
This paper mainly focuses on the rain streak removal task from a single image. Based on the observation that the distribution of rain streak in an image is sparse. We propose a two- phase single image deraining method. Firstly, it detects the rain locations with a proposed anisotropic global gradient prior (AGGP) and generates a rain mask for rain streak removal. Then it recovers the information in rain distorted region with AGGP based multi-layer image inpainting model. Furthermore, to solve the multi-variable optimization problems, we develop an alternating half-quadratic algorithm by introducing alternating algorithm and the variable split method. Both experiments on synthesized and realworld images show the proposed method outperforms state-of-the-art methods in terms of rain streak removal and image multi-layer information preservation.
Image deblurring and inpainting are traditional image processing problems, and the effects achieved for high-resolution images are not satisfactory. In recent years, Convolutional Sparse coding (CSC) has been received more attention and introduced into image processing, such as blind deblurring. However, none of the works address the issue containing both blur and inpainting. In this work, we propose a novel framework of CSC for simultaneous image deblurring and inpainting. First, we learn a dictionary instead of applying a given dictionary for better image representation. Second, we use the learned dictionary with the ℓ1 norm to regularize images. In addition, we apply a total anisotropic variation to enhance the edges of the image. Usually, we use the alternating direction method of multipliers (ADMM) formulation in the Fourier domain for the dictionary. We demonstrate the proposed training scheme for simultaneous image deblurring and inpainting, achieving state-of-the-art results.
In this paper, we address the rain streak removal from a single image. In order to efficiently detect and remove the annoying rain streaks, we propose a global single-directional gradient prior with the L0 norm to model the rain streak. To preserve the abundant information of the background, we learn a convolutional sparse coding (CSC) to represent the background. Furthermore, we develop an alternating direction method of multipliers (ADMM) to solve multi-variable optimization problems. Experiments on synthesized and real-world images show that the proposed method outperforms state-of-art methods in terms of rain streak removal and background preservation.
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