In this paper, we study design of deep neural networks for tasks of image restoration. We propose a novel style of residual connections dubbed "dual residual connection", which exploits the potential of paired operations, e.g., up- and down-sampling or convolution with large- and small-size kernels. We design a modular block implementing this connection style; it is equipped with two containers to which arbitrary paired operations are inserted. Adopting the "unraveled" view of the residual networks proposed by , we point out that a stack of the proposed modular blocks allows the first operation in a block interact with the second operation in any subsequent blocks. Specifying the two operations in each of the stacked blocks, we build a complete network for each individual task of image restoration. We experimentally evaluate the proposed approach on five image restoration tasks using nine datasets. The results show that the proposed networks with properly chosen paired operations outperform previous methods on almost all of the tasks and datasets.
Raindrop detection and removal Various approaches @cite_56 @cite_9 @cite_22 @cite_6 @cite_3 have been proposed to tackle this problem in the literature. Kurihata al @cite_56 proposed to detect raindrops with raindrop-templates learned using PCA. Ramensh @cite_22 proposed a method based on K-Means clustering and median filtering to estimate clear images. Recently, Qian al @cite_21 proposed a hybrid network consisting of a convolutional-LSTM for localizing raindrops and a CNN for generating clear images, which is trained in a GAN framework.
@cite_22 @cite_9 @cite_21 @cite_3 @cite_6 @cite_56
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A novel hybrid algorithm has been proposed in this paper to detect raindrops, remove and then restore the image back ground in a single image. This new hybrid algorithm is framed based on K-Means clustering and Median filtering for the fast retrieval of Rain droplets from the single image. K-Means clustering algorithm is an efficient algorithm for image clustering. The algorithm proposed in this paper has different approach from other established numerical schemes. The hybrid algorithm is framed in order to identify the rain droplets using clustering and shape modeling of raindrops. The proposed system is fast compared with alternate droplet identification schemes. The process allows rapid evaluation of the contour of the raindrops and convergence to its final resultant with very little iteration. The experiments demonstrate the efficiency and accuracy of the method. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. A new form of document coupling called co-citation is defined as the frequency with which two documents are cited together. The co-citation frequency of two scientific papers can be determined by comparing lists of citing documents in the Science Citation Index and counting identical entries. Networks of co-cited papers can be generated for specific scientific specialties, and an example is drawn from the literature of particle physics. Co-citation patterns are found to differ significantly from bibliographic coupling patterns, but to agree generally with patterns of direct citation. Clusters of co-cited papers provide a new way to study the specialty structure of science. They may provide a new approach to indexing and to the creation of SDI profiles.