Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
Kai Zhang,Wangmeng Zuo,Lei Zhang +2 more
- pp 3262-3271
TLDR
Extensive experimental results show that the proposed convolutional super-resolution network not only can produce favorable results on multiple degradations but also is computationally efficient, providing a highly effective and scalable solution to practical SISR applications.Abstract:
Recent years have witnessed the unprecedented success of deep convolutional neural networks (CNNs) in single image super-resolution (SISR). However, existing CNN-based SISR methods mostly assume that a low-resolution (LR) image is bicubicly downsampled from a high-resolution (HR) image, thus inevitably giving rise to poor performance when the true degradation does not follow this assumption. Moreover, they lack scalability in learning a single model to nonblindly deal with multiple degradations. To address these issues, we propose a general framework with dimensionality stretching strategy that enables a single convolutional super-resolution network to take two key factors of the SISR degradation process, i.e., blur kernel and noise level, as input. Consequently, the super-resolver can handle multiple and even spatially variant degradations, which significantly improves the practicability. Extensive experimental results on synthetic and real LR images show that the proposed convolutional super-resolution network not only can produce favorable results on multiple degradations but also is computationally efficient, providing a highly effective and scalable solution to practical SISR applications.read more
Citations
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Proceedings ArticleDOI
Residual Dense Network for Image Super-Resolution
TL;DR: This paper proposes residual dense block (RDB) to extract abundant local features via dense connected convolutional layers and uses global feature fusion in RDB to jointly and adaptively learn global hierarchical features in a holistic way.
Posted Content
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
TL;DR: This work proposes a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections, and proposes a channel attention mechanism to adaptively rescale channel-wise features by considering interdependencies among channels.
Book ChapterDOI
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
TL;DR: Very deep residual channel attention networks (RCAN) as mentioned in this paper proposes a residual in residual (RIR) structure to form very deep network, which consists of several residual groups with long skip connections Each residual group contains some residual blocks with short skip connections.
Proceedings ArticleDOI
Second-Order Attention Network for Single Image Super-Resolution
TL;DR: Experimental results demonstrate the superiority of the SAN network over state-of-the-art SISR methods in terms of both quantitative metrics and visual quality.
Journal ArticleDOI
Deep Learning for Image Super-Resolution: A Survey
TL;DR: A survey on recent advances of image super-resolution techniques using deep learning approaches in a systematic way, which can roughly group the existing studies of SR techniques into three major categories: supervised SR, unsupervised SR, and domain-specific SR.
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