A Tree-guided CNN for image super-resolution

• URL: http://arxiv.org/abs/2506.02585v1
• Date: Tue, 03 Jun 2025 08:05:11 GMT
• Title: A Tree-guided CNN for image super-resolution
• Authors: Chunwei Tian, Mingjian Song, Xiaopeng Fan, Xiangtao Zheng, Bob Zhang, David Zhang,
• Abstract summary: We design a tree-guided CNN for image super-resolution (TSRNet)<n>It uses a tree architecture to guide a deep network to enhance effect of key nodes to amplify the relation of hierarchical information.<n>To prevent insufficiency of the obtained structural information, cosine transform techniques in the TSRNet are used to improve performance of image super-resolution.
• Score: 50.30242741813306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep convolutional neural networks can extract more accurate structural information via deep architectures to obtain good performance in image super-resolution. However, it is not easy to find effect of important layers in a single network architecture to decrease performance of super-resolution. In this paper, we design a tree-guided CNN for image super-resolution (TSRNet). It uses a tree architecture to guide a deep network to enhance effect of key nodes to amplify the relation of hierarchical information for improving the ability of recovering images. To prevent insufficiency of the obtained structural information, cosine transform techniques in the TSRNet are used to extract cross-domain information to improve the performance of image super-resolution. Adaptive Nesterov momentum optimizer (Adan) is applied to optimize parameters to boost effectiveness of training a super-resolution model. Extended experiments can verify superiority of the proposed TSRNet for restoring high-quality images. Its code can be obtained at https://github.com/hellloxiaotian/TSRNet.

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)
• Image super-resolution via dynamic network [19.404066956727885]
  We present a dynamic network for image super-resolution (DSRNet) It contains a residual enhancement block, wide enhancement block, feature refinement block and construction block. Our method is more competitive in terms of performance and recovering time of image super-resolution and complexity.
  arXiv  Detail & Related papers  (2023-10-16T13:56:56Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• 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)
• Pushing the Efficiency Limit Using Structured Sparse Convolutions [82.31130122200578]
  We propose Structured Sparse Convolution (SSC), which leverages the inherent structure in images to reduce the parameters in the convolutional filter. We show that SSC is a generalization of commonly used layers (depthwise, groupwise and pointwise convolution) in efficient architectures'' Architectures based on SSC achieve state-of-the-art performance compared to baselines on CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet classification benchmarks.
  arXiv  Detail & Related papers  (2022-10-23T18:37:22Z)
• Real Image Restoration via Structure-preserving Complementarity Attention [10.200625895876023]
  We propose a novel lightweight Complementary Attention Module, which includes a density module and a sparse module. To reduce the loss of details caused by denoising, this paper constructs a gradient-based structure-preserving branch.
  arXiv  Detail & Related papers  (2022-07-28T04:24:20Z)
• 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)
• 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)

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.