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FAN: Frequency Aggregation Network for Real Image Super-resolution
Single image super-resolution (SISR) aims to recover the high-resolution...
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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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Adaptive Densely Connected Super-Resolution Reconstruction
For a better performance in single image super-resolution(SISR), we pres...
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A Frequency Domain Neural Network for Fast Image Super-resolution
In this paper, we present a frequency domain neural network for image su...
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Adaptive Neural Layer for Globally Filtered Segmentation
This study is motivated by typical images taken during ultrasonic examin...
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Extracting Super-resolution Structures inside a Single Molecule or Overlapped Molecules from One Blurred Image
In some super-resolution techniques, adjacent points are illuminated at ...
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Image Super-Resolution Using Attention Based DenseNet with Residual Deconvolution
Image super-resolution is a challenging task and has attracted increasin...
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Learning Omni-frequency Region-adaptive Representations for Real Image Super-Resolution
Traditional single image super-resolution (SISR) methods that focus on solving single and uniform degradation (i.e., bicubic down-sampling), typically suffer from poor performance when applied into real-world low-resolution (LR) images due to the complicated realistic degradations. The key to solving this more challenging real image super-resolution (RealSR) problem lies in learning feature representations that are both informative and content-aware. In this paper, we propose an Omni-frequency Region-adaptive Network (ORNet) to address both challenges, here we call features of all low, middle and high frequencies omni-frequency features. Specifically, we start from the frequency perspective and design a Frequency Decomposition (FD) module to separate different frequency components to comprehensively compensate the information lost for real LR image. Then, considering the different regions of real LR image have different frequency information lost, we further design a Region-adaptive Frequency Aggregation (RFA) module by leveraging dynamic convolution and spatial attention to adaptively restore frequency components for different regions. The extensive experiments endorse the effective, and scenario-agnostic nature of our OR-Net for RealSR.
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