Image Super-resolution with An Enhanced Group Convolutional Neural Network

• URL: http://arxiv.org/abs/2205.14548v1
• Date: Sun, 29 May 2022 00:34:25 GMT
• Title: Image Super-resolution with An Enhanced Group Convolutional Neural Network
• Authors: Chunwei Tian, Yixuan Yuan, Shichao Zhang, Chia-Wen Lin, Wangmeng Zuo, David Zhang
• Abstract summary: 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.
• Score: 102.2483249598621
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image super-resolution, which may increase computational cost in general. In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR). Also, a signal enhancement operation in the ESRGCNN is useful to inherit more long-distance contextual information for resolving long-term dependency. An adaptive up-sampling operation is gathered into a CNN to obtain an image super-resolution model with low-resolution images of different sizes. Extensive 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. Code is found at https://github.com/hellloxiaotian/ESRGCNN.

Related papers

• Adaptive Convolutional Neural Network for Image Super-resolution [43.06377001247278]
  We propose a adaptive convolutional neural network for image super-resolution (ADSRNet) The upper network can enhance relation of context information, salient information relation of a kernel mapping and relations of shallow and deep layers. The lower network utilizes a symmetric architecture to enhance relations of different layers to mine more structural information.
  arXiv  Detail & Related papers  (2024-02-24T03:44:06Z)
• 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)
• A heterogeneous group CNN for image super-resolution [127.2132400582117]
  Convolutional neural networks (CNNs) have obtained remarkable performance via deep architectures. We present a heterogeneous group SR CNN (HGSRCNN) via leveraging structure information of different types to obtain a high-quality image.
  arXiv  Detail & Related papers  (2022-09-26T04:14:59Z)
• 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)
• Asymmetric CNN for image super-resolution [102.96131810686231]
  Deep convolutional neural networks (CNNs) have been widely applied for low-level vision over the past five years. We propose an asymmetric CNN (ACNet) comprising an asymmetric block (AB), a mem?ory enhancement block (MEB) and a high-frequency feature enhancement block (HFFEB) for image super-resolution. Our ACNet can effectively address single image super-resolution (SISR), blind SISR and blind SISR of blind noise problems.
  arXiv  Detail & Related papers  (2021-03-25T07:10:46Z)
• Cascade Convolutional Neural Network for Image Super-Resolution [15.650515790147189]
  We propose a cascaded convolution neural network for image super-resolution (CSRCNN) Images of different scales can be trained simultaneously and the learned network can make full use of the information resided in different scales of images.
  arXiv  Detail & Related papers  (2020-08-24T11:34:03Z)
• Lightweight image super-resolution with enhanced CNN [82.36883027158308]
  Deep convolutional neural networks (CNNs) with strong expressive ability have achieved impressive performances on single image super-resolution (SISR) We propose a lightweight enhanced SR CNN (LESRCNN) with three successive sub-blocks, an information extraction and enhancement block (IEEB), a reconstruction block (RB) and an information refinement block (IRB) IEEB extracts hierarchical low-resolution (LR) features and aggregates the obtained features step-by-step to increase the memory ability of the shallow layers on deep layers for SISR. RB converts low-frequency features into high-frequency features by fusing global
  arXiv  Detail & Related papers  (2020-07-08T18:03:40Z)
• Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction [14.217260022873083]
  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.
  arXiv  Detail & Related papers  (2020-04-28T17:23:46Z)
• 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.