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Image Super-Resolution Using Very Deep Residual Channel Attention Networks

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.
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Residual Dense Network for Image Super-Resolution

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.
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NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results

This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results and gauges the state-of-the-art in single imagesuper-resolution.
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Residual Non-local Attention Networks for Image Restoration

The proposed residual local and non-local attention learning to train the very deep network is generalized for various image restoration applications, such as image denoising, demosaicing, compression artifacts reduction, and super-resolution.
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Visual Semantic Reasoning for Image-Text Matching

A simple and interpretable reasoning model to generate visual representation that captures key objects and semantic concepts of a scene that outperforms the current best method for image retrieval and caption retrieval on MS-COCO and Flickr30K datasets.
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Residual Dense Network for Image Restoration

This work proposes residual dense block (RDB) to extract abundant local features via densely connected convolutional layers and proposes local feature fusion in RDB to adaptively learn more effective features from preceding and current local features and stabilize the training of wider network.
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TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution

Experimental results demonstrate that theTDAN is capable of alleviating occlusions and artifacts for temporal alignment and the TDAN-based VSR model outperforms several recent state-of-the-art VSR networks with a comparable or even much smaller model size.
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Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution

A one-stage space-time video super-resolution framework is proposed, which directly synthesizes an HR slow-motion video from an LFR, LR video and proposes a deformable ConvLSTM to align and aggregate temporal information simultaneously for better leveraging global temporal contexts.
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Multimodal Style Transfer via Graph Cuts

This paper introduces a more flexible and general universal style transfer technique: multimodal style transfer (MST), which explicitly considers the matching of semantic patterns in content and style images.
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Channel Splitting Network for Single MR Image Super-Resolution

The extensive experiments on various MR images, including proton density (PD), T1, and T2 images, show that the proposed CSN model achieves superior performance over other state-of-the-art SISR methods.
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