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Gated Fusion Network for Joint Image Deblurring and Super-Resolution
Single-image super-resolution is a fundamental task for vision applicati...
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Deep Super-Resolution Network for Single Image Super-Resolution with Realistic Degradations
Single Image Super-Resolution (SISR) aims to generate a high-resolution ...
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Learning regularization and intensity-gradient-based fidelity for single image super resolution
How to extract more and useful information for single image super resolu...
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Single Image Super-Resolution via a Holistic Attention Network
Informative features play a crucial role in the single image super-resol...
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Fast and Robust Cascade Model for Multiple Degradation Single Image Super-Resolution
Single Image Super-Resolution (SISR) is one of the low-level computer vi...
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Super-Resolution DOA Estimation for Arbitrary Array Geometries Using a Single Noisy Snapshot
We address the problem of search-free DOA estimation from a single noisy...
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Multi-modal Datasets for Super-resolution
Nowdays, most datasets used to train and evaluate super-resolution model...
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Gated Fusion Network for Degraded Image Super Resolution
Single image super resolution aims to enhance image quality with respect to spatial content, which is a fundamental task in computer vision. In this work, we address the task of single frame super resolution with the presence of image degradation, e.g., blur, haze, or rain streaks. Due to the limitations of frame capturing and formation processes, image degradation is inevitable, and the artifacts would be exacerbated by super resolution methods. To address this problem, we propose a dual-branch convolutional neural network to extract base features and recovered features separately. The base features contain local and global information of the input image. On the other hand, the recovered features focus on the degraded regions and are used to remove the degradation. Those features are then fused through a recursive gate module to obtain sharp features for super resolution. By decomposing the feature extraction step into two task-independent streams, the dual-branch model can facilitate the training process by avoiding learning the mixed degradation all-in-one and thus enhance the final high-resolution prediction results. We evaluate the proposed method in three degradation scenarios. Experiments on these scenarios demonstrate that the proposed method performs more efficiently and favorably against the state-of-the-art approaches on benchmark datasets.
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