Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction

• URL: http://arxiv.org/abs/2004.13674v1
• Date: Tue, 28 Apr 2020 17:23:46 GMT
• Title: Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction
• Authors: Jie Cai, Zibo Meng, Chiu Man Ho
• Abstract summary: As the deep networks go deeper, they become more difficult to train and more difficult to restore the finer texture details. We propose a Residual Channel Attention-Generative Adversarial Network (RCA-GAN) to solve these problems. RCA-GAN yields consistently better visual quality with more detailed and natural textures than baseline models.
• Score: 14.217260022873083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image super-resolution is one of the important computer vision techniques aiming to reconstruct high-resolution images from corresponding low-resolution ones. Most recently, deep learning-based approaches have been demonstrated for image super-resolution. However, as the deep networks go deeper, they become more difficult to train and more difficult to restore the finer texture details, especially under real-world settings. In this paper, we propose a Residual Channel Attention-Generative Adversarial Network(RCA-GAN) to solve these problems. Specifically, a novel residual channel attention block is proposed to form RCA-GAN, which consists of a set of residual blocks with shortcut connections, and a channel attention mechanism to model the interdependence and interaction of the feature representations among different channels. Besides, a generative adversarial network (GAN) is employed to further produce realistic and highly detailed results. Benefiting from these improvements, the proposed RCA-GAN yields consistently better visual quality with more detailed and natural textures than baseline models; and achieves comparable or better performance compared with the state-of-the-art methods for real-world image super-resolution.

Related papers

• An Advanced Features Extraction Module for Remote Sensing Image Super-Resolution [0.5461938536945723]
  We propose an advanced feature extraction module called Channel and Spatial Attention Feature Extraction (CSA-FE) Our proposed method helps the model focus on the specific channels and spatial locations containing high-frequency information so that the model can focus on relevant features and suppress irrelevant ones. Our model achieved superior performance compared to various existing models.
  arXiv  Detail & Related papers  (2024-05-07T18:15:51Z)
• Cross-View Hierarchy Network for Stereo Image Super-Resolution [14.574538513341277]
  Stereo image super-resolution aims to improve the quality of high-resolution stereo image pairs by exploiting complementary information across views. We propose a novel method, named Cross-View-Hierarchy Network for Stereo Image Super-Resolution (CVHSSR) CVHSSR achieves the best stereo image super-resolution performance than other state-of-the-art methods while using fewer parameters.
  arXiv  Detail & Related papers  (2023-04-13T03:11:30Z)
• RDRN: Recursively Defined Residual Network for Image Super-Resolution [58.64907136562178]
  Deep convolutional neural networks (CNNs) have obtained remarkable performance in single image super-resolution. We propose a novel network architecture which utilizes attention blocks efficiently.
  arXiv  Detail & Related papers  (2022-11-17T11:06:29Z)
• Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution [64.15915577164894]
  A hierarchical image super-resolution network (HSRNet) is proposed to suppress the influence of aliasing. HSRNet achieves better quantitative and visual performance than other works, and remits the aliasing more effectively.
  arXiv  Detail & Related papers  (2022-06-07T14:55:32Z)
• Image Super-resolution with An Enhanced Group Convolutional Neural Network [102.2483249598621]
  CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. We present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture. Experiments report that our ESRGCNN surpasses the state-of-the-arts in terms of SISR performance, complexity, execution speed, image quality evaluation and visual effect in SISR.
  arXiv  Detail & Related papers  (2022-05-29T00:34:25Z)
• Deep Networks for Image and Video Super-Resolution [30.75380029218373]
  Single image super-resolution (SISR) is built using efficient convolutional units we refer to as mixed-dense connection blocks (MDCB) We train two versions of our network to enhance complementary image qualities using different loss configurations. We further employ our network for super-resolution task, where our network learns to aggregate information from multiple frames and maintain-temporal consistency.
  arXiv  Detail & Related papers  (2022-01-28T09:15:21Z)
• Interpretable Detail-Fidelity Attention Network for Single Image Super-Resolution [89.1947690981471]
  We propose a purposeful and interpretable detail-fidelity attention network to progressively process smoothes and details in divide-and-conquer manner. Particularly, we propose a Hessian filtering for interpretable feature representation which is high-profile for detail inference. Experiments demonstrate that the proposed methods achieve superior performances over the state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-09-28T08:31:23Z)
• iSeeBetter: Spatio-temporal video super-resolution using recurrent generative back-projection networks [0.0]
  We present iSeeBetter, a novel GAN-based structural-temporal approach to video super-resolution (VSR) iSeeBetter extracts spatial and temporal information from the current and neighboring frames using the concept of recurrent back-projection networks as its generator. Our results demonstrate that iSeeBetter offers superior VSR fidelity and surpasses state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-06-13T01:36:30Z)
• Iterative Network for Image Super-Resolution [69.07361550998318]
  Single image super-resolution (SISR) has been greatly revitalized by the recent development of convolutional neural networks (CNN) This paper provides a new insight on conventional SISR algorithm, and proposes a substantially different approach relying on the iterative optimization. A novel iterative super-resolution network (ISRN) is proposed on top of the iterative optimization.
  arXiv  Detail & Related papers  (2020-05-20T11:11:47Z)
• Learning Enriched Features for Real Image Restoration and Enhancement [166.17296369600774]
  convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. We present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network. Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details.
  arXiv  Detail & Related papers  (2020-03-15T11:04:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.