DEA-Net: Single image dehazing based on detail-enhanced convolution and content-guided attention

• URL: http://arxiv.org/abs/2301.04805v1
• Date: Thu, 12 Jan 2023 04:27:22 GMT
• Title: DEA-Net: Single image dehazing based on detail-enhanced convolution and content-guided attention
• Authors: Zixuan Chen, Zewei He, Zhe-Ming Lu
• Abstract summary: We propose a detail-enhanced attention block (DEAB) consisting of the detail-enhanced convolution (DEConv) and the content-guided attention (CGA) By combining these components, we propose our detail-enhanced attention network (DEA-Net) for recovering high-quality haze-free images.
• Score: 16.33443834279481
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single image dehazing is a challenging ill-posed problem which estimates latent haze-free images from observed hazy images. Some existing deep learning based methods are devoted to improving the model performance via increasing the depth or width of convolution. The learning ability of convolutional neural network (CNN) structure is still under-explored. In this paper, a detail-enhanced attention block (DEAB) consisting of the detail-enhanced convolution (DEConv) and the content-guided attention (CGA) is proposed to boost the feature learning for improving the dehazing performance. Specifically, the DEConv integrates prior information into normal convolution layer to enhance the representation and generalization capacity. Then by using the re-parameterization technique, DEConv is equivalently converted into a vanilla convolution with NO extra parameters and computational cost. By assigning unique spatial importance map (SIM) to every channel, CGA can attend more useful information encoded in features. In addition, a CGA-based mixup fusion scheme is presented to effectively fuse the features and aid the gradient flow. By combining above mentioned components, we propose our detail-enhanced attention network (DEA-Net) for recovering high-quality haze-free images. Extensive experimental results demonstrate the effectiveness of our DEA-Net, outperforming the state-of-the-art (SOTA) methods by boosting the PSNR index over 41 dB with only 3.653 M parameters. The source code of our DEA-Net will be made available at https://github.com/cecret3350/DEA-Net.

Related papers

• A Tree-guided CNN for image super-resolution [50.30242741813306]
  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.
  arXiv  Detail & Related papers  (2025-06-03T08:05:11Z)
• CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution [55.94314421887744]
  Lightweight image super-resolution (SR) methods aim at increasing the resolution and restoring the details of an image using a lightweight neural network. Our analysis reveals that these methods are hindered by constrained feature diversity, which adversely impacts feature representation and detail recovery. We propose a simple yet effective baseline called CubeFormer, designed to enhance feature richness by completing holistic information aggregation.
  arXiv  Detail & Related papers  (2024-12-03T08:02:26Z)
• MGHF: Multi-Granular High-Frequency Perceptual Loss for Image Super-Resolution [0.3958317527488535]
  We propose an invertible neural network (INN)-based naive textbfMulti-textbfGranular textbfHigh-textbfFrequency (MGHF-n) perceptual loss trained on ImageNet to overcome these issues.<n>We develop a comprehensive framework with several constraints to preserve, prioritize, and regularize information across multiple perspectives.
  arXiv  Detail & Related papers  (2024-11-20T18:56:24Z)
• SODAWideNet++: Combining Attention and Convolutions for Salient Object Detection [3.2586315449885106]
  We propose a novel encoder-decoder-style neural network called SODAWideNet++ designed explicitly for Salient Object Detection. Inspired by the vision transformers ability to attain a global receptive field from the initial stages, we introduce the Attention Guided Long Range Feature Extraction (AGLRFE) module. In contrast to the current paradigm of ImageNet pre-training, we modify 118K annotated images from the COCO semantic segmentation dataset by binarizing the annotations to pre-train the proposed model end-to-end.
  arXiv  Detail & Related papers  (2024-08-29T15:51:06Z)
• PGNeXt: High-Resolution Salient Object Detection via Pyramid Grafting Network [24.54269823691119]
  We present an advanced study on more challenging high-resolution salient object detection (HRSOD) from both dataset and network framework perspectives. To compensate for the lack of HRSOD dataset, we thoughtfully collect a large-scale high resolution salient object detection dataset, called UHRSD. All the images are finely annotated in pixel-level, far exceeding previous low-resolution SOD datasets.
  arXiv  Detail & Related papers  (2024-08-02T09:31:21Z)
• AMSA-UNet: An Asymmetric Multiple Scales U-net Based on Self-attention for Deblurring [7.00986132499006]
  asymmetric multiple scales U-net based on self-attention (AMSA-UNet) is proposed to improve the accuracy and computational complexity. By introducing a multiple-scales U shape architecture, the network can focus on blurry regions at the global level and better recover image details at the local level.
  arXiv  Detail & Related papers  (2024-06-13T11:39:02Z)
• A Semantic-Aware and Multi-Guided Network for Infrared-Visible Image Fusion [41.34335755315773]
  This paper focuses on how to model correlation-driven decomposing features and reason high-level graph representation.<n>We propose a three-branch encoder-decoder architecture along with corresponding fusion layers as the fusion strategy.<n> Experiments demonstrate the competitive results compared with state-of-the-art methods in visible/infrared image fusion and medical image fusion tasks.
  arXiv  Detail & Related papers  (2024-06-11T09:32:40Z)
• 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)
• Deep Multi-Threshold Spiking-UNet for Image Processing [51.88730892920031]
  This paper introduces the novel concept of Spiking-UNet for image processing, which combines the power of Spiking Neural Networks (SNNs) with the U-Net architecture. To achieve an efficient Spiking-UNet, we face two primary challenges: ensuring high-fidelity information propagation through the network via spikes and formulating an effective training strategy. Experimental results show that, on image segmentation and denoising, our Spiking-UNet achieves comparable performance to its non-spiking counterpart.
  arXiv  Detail & Related papers  (2023-07-20T16:00:19Z)
• Rank-Enhanced Low-Dimensional Convolution Set for Hyperspectral Image Denoising [50.039949798156826]
  This paper tackles the challenging problem of hyperspectral (HS) image denoising. We propose rank-enhanced low-dimensional convolution set (Re-ConvSet) We then incorporate Re-ConvSet into the widely-used U-Net architecture to construct an HS image denoising method.
  arXiv  Detail & Related papers  (2022-07-09T13:35:12Z)
• Cross-receptive Focused Inference Network for Lightweight Image Super-Resolution [64.25751738088015]
  Transformer-based methods have shown impressive performance in single image super-resolution (SISR) tasks. Transformers that need to incorporate contextual information to extract features dynamically are neglected. We propose a lightweight Cross-receptive Focused Inference Network (CFIN) that consists of a cascade of CT Blocks mixed with CNN and Transformer.
  arXiv  Detail & Related papers  (2022-07-06T16:32:29Z)
• Learning Enriched Features for Fast Image Restoration and Enhancement [166.17296369600774]
  This paper presents a holistic goal of maintaining spatially-precise high-resolution representations through the entire network. We learn an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details. Our approach achieves state-of-the-art results for a variety of image processing tasks, including defocus deblurring, image denoising, super-resolution, and image enhancement.
  arXiv  Detail & Related papers  (2022-04-19T17:59:45Z)
• 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)
• CNN based Multistage Gated Average Fusion (MGAF) for Human Action Recognition Using Depth and Inertial Sensors [1.52292571922932]
  Convolutional Neural Network (CNN) provides leverage to extract and fuse features from all layers of its architecture. We propose novel Multistage Gated Average Fusion (MGAF) network which extracts and fuses features from all layers of CNN.
  arXiv  Detail & Related papers  (2020-10-29T11:49:13Z)

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.