Given a single input rainy image, our goal is to visually remove rain streaks and the veiling effect caused by scattering and transmission of rain streaks and rain droplets. We are particularly concerned with heavy rain, where rain streaks of various sizes and directions can overlap each other and the veiling effect reduces contrast severely. To achieve our goal, we introduce a scale-aware multi-stage convolutional neural network. Our main idea here is that different sizes of rain-streaks visually degrade the scene in different ways. Large nearby streaks obstruct larger regions and are likely to reflect specular highlights more prominently than smaller distant streaks. These different effects of different streaks have their own characteristics in their image features, and thus need to be treated differently. To realize this, we create parallel sub-networks that are trained and made aware of these different scales of rain streaks. To our knowledge, this idea of parallel sub-networks that treats the same class of objects according to their unique sub-classes is novel, particularly in the context of rain removal. To verify our idea, we conducted experiments on both synthetic and real images, and found that our method is effective and outperforms the state-of-the-art methods.
For single-image rain streak removal, 's @cite_3 introduces a method that decomposes an input image into its low frequency component (structure layer) and a high-frequency component (texture layer). The high-frequency layer, which contains rain streaks, is used to extract rain streaks component and background details using sparse-coding based dictionary learning. 's @cite_12 proposes a discriminative sparse coding framework. Its objective function employs a dictionary, its background coefficients and rain layer coefficients. The goal of the objective function is to learn the background and rain layer by forcing the coefficient vector to be sparse.
@cite_12 @cite_3
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Single image rain streaks removal has recently witnessed substantial progress due to the development of deep convolutional neural networks. However, existing deep learning based methods either focus on the entrance and exit of the network by decomposing the input image into high and low frequency information and employing residual learning to reduce the mapping range, or focus on the introduction of cascaded learning scheme to decompose the task of rain streaks removal into multi-stages. These methods treat the convolutional neural network as an encapsulated end-to-end mapping module without deepening into the rationality and superiority of neural network design. In this paper, we delve into an effective end-to-end neural network structure for stronger feature expression and spatial correlation learning. Specifically, we propose a non-locally enhanced encoder-decoder network framework, which consists of a pooling indices embedded encoder-decoder network to efficiently learn increasingly abstract feature representation for more accurate rain streaks modeling while perfectly preserving the image detail. The proposed encoder-decoder framework is composed of a series of non-locally enhanced dense blocks that are designed to not only fully exploit hierarchical features from all the convolutional layers but also well capture the long-distance dependencies and structural information. Extensive experiments on synthetic and real datasets demonstrate that the proposed method can effectively remove rain-streaks on rainy image of various densities while well preserving the image details, which achieves significant improvements over the recent state-of-the-art methods. We propose a novel Convolutional Coding-based Rain Removal (CCRR) algorithm for automatically removing rain streaks from a single rainy image. Our method first learns a set of generic sparsity-based and low-rank representation-based convolutional filters for efficiently representing background clear image and rain streaks, respectively. To this end, we first develop a new method for learning a set of convolutional low-rank filters. Then, using these learned filter, we propose an optimization problem to decompose a rainy image into a clear background image and a rain streak image. By working directly on the whole image, the proposed rain streak removal algorithm does not need to divide the image into overlapping patches for leaning local dictionaries. Extensive experiments on synthetic and real images show that the proposed method performs favorably compared to state-of-the-art rain streak removal algorithms. In this paper, we address a rain removal problem from a single image, even in the presence of heavy rain and rain accumulation. Our core ideas lie in our new rain image models and a novel deep learning architecture. We first modify the commonly used model, which is a linear combination of a rain streak layer and a background layer, by adding a binary map that locates rain streak regions. Second, we create a model consisting of a component representing rain accumulation (where individual streaks cannot be seen, and thus visually similar to mist or fog), and another component representing various shapes and directions of overlapping rain streaks, which normally happen in heavy rain. Based on the first model, we develop a multi-task deep learning architecture that learns the binary rain streak map, the appearance of rain streaks, and the clean background, which is our ultimate output. The additional binary map is critically beneficial, since its loss function can provide additional strong information to the network. In many cases though, rain streaks can be dense and large in their size, thus to obtain the clean background, we need spatial contextual information. For this, we utilize the dilated convolution. To handle rain accumulation (again, a phenomenon visually similar to mist or fog) and various shapes and directions of overlapping rain streaks, we propose an iterative information feedback (IIF) network that removes rain streaks and clears up the rain accumulation iteratively and progressively. Overall, this multi-task learning and iterative information feedback benefits each other and constitutes a network that is end-to-end trainable. Our extensive evaluation on real images, particularly on heavy rain, shows the effectiveness of our novel models and architecture, outperforming the state-of-the-art methods significantly. This paper addresses the problem of line pattern noise removal from a single image, such as rain streak, hyperspectral stripe and so on. Most of the previous methods model the line pattern noise in original image domain, which fail to explicitly exploit the directional characteristic, thus resulting in a redundant subspace with poor representation ability for those line pattern noise. To achieve a compact subspace for the line pattern structure, in this work, we incorporate a transformation into the image decomposition model so that maps the input image to a domain where the line pattern appearance has an extremely distinct low-rank structure, which naturally allows us to enforce a low-rank prior to extract the line pattern streak stripe from the noisy image. Moreover, the random noise is usually mixed up with the line pattern noise, which makes the challenging problem much more difficult. While previous methods resort to the spectral or temporal correlation of the multi-images, we give a detailed analysis between the noisy and clean image in both local gradient and nonlocal domain, and propose a compositional directional total variational and low-rank prior for the image layer, thus to simultaneously accommodate both types of noise. The proposed method has been evaluated on two different tasks, including remote sensing image mixed random-stripe noise removal and rain streak removal, all of which obtain very impressive performances.