Emphasizing Crucial Features for Efficient Image Restoration
- URL: http://arxiv.org/abs/2405.11468v1
- Date: Sun, 19 May 2024 07:04:05 GMT
- Title: Emphasizing Crucial Features for Efficient Image Restoration
- Authors: Hu Gao, Bowen Ma, Ying Zhang, Jingfan Yang, Jing Yang, Depeng Dang,
- Abstract summary: We propose a framework to adapt to varying degrees of degradation across different regions for image restoration.
Specifically, we design a spatial and frequency attention mechanism (SFAM) to emphasize crucial features for restoration.
We also propose our ECFNet, which integrates the aforementioned components into a U-shaped backbone for recovering high-quality images.
- Score: 6.204240924744974
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Image restoration is a challenging ill-posed problem which estimates latent sharp image from its degraded counterpart. Although the existing methods have achieved promising performance by designing novelty architecture of module, they ignore the fact that different regions in a corrupted image undergo varying degrees of degradation. In this paper, we propose an efficient and effective framework to adapt to varying degrees of degradation across different regions for image restoration. Specifically, we design a spatial and frequency attention mechanism (SFAM) to emphasize crucial features for restoration. SFAM consists of two modules: the spatial domain attention module (SDAM) and the frequency domain attention module (FDAM). The SFAM discerns the degradation location through spatial selective attention and channel selective attention in the spatial domain, while the FDAM enhances high-frequency signals to amplify the disparities between sharp and degraded image pairs in the spectral domain. Additionally, to capture global range information, we introduce a multi-scale block (MSBlock) that consists of three scale branches, each containing multiple simplified channel attention blocks (SCABlocks) and a multi-scale feed-forward block (MSFBlock). Finally, we propose our ECFNet, which integrates the aforementioned components into a U-shaped backbone for recovering high-quality images. Extensive experimental results demonstrate the effectiveness of ECFNet, outperforming state-of-the-art (SOTA) methods on both synthetic and real-world datasets.
Related papers
- FE-UNet: Frequency Domain Enhanced U-Net with Segment Anything Capability for Versatile Image Segmentation [50.9040167152168]
We experimentally quantify the contrast sensitivity function of CNNs and compare it with that of the human visual system.
We propose the Wavelet-Guided Spectral Pooling Module (WSPM) to enhance and balance image features across the frequency domain.
To further emulate the human visual system, we introduce the Frequency Domain Enhanced Receptive Field Block (FE-RFB)
We develop FE-UNet, a model that utilizes SAM2 as its backbone and incorporates Hiera-Large as a pre-trained block.
arXiv Detail & Related papers (2025-02-06T07:24:34Z) - Image Forgery Localization via Guided Noise and Multi-Scale Feature Aggregation [13.610095493539397]
We propose a guided and multi-scale feature aggregated network for IFL.
In order to learn the noise feature under different types of forgery, we develop an effective noise extraction module.
Then, we design a Feature Aggregation Module (FAM) that uses dynamic convolution to adaptively aggregate RGB and noise features over multiple scales.
Finally, we propose an Atrous Residual Pyramid Module (ARPM) to enhance features representation and capture both global and local features.
arXiv Detail & Related papers (2024-11-17T11:50:09Z) - Multi-Scale Representation Learning for Image Restoration with State-Space Model [13.622411683295686]
We propose a novel Multi-Scale State-Space Model-based (MS-Mamba) for efficient image restoration.
Our proposed method achieves new state-of-the-art performance while maintaining low computational complexity.
arXiv Detail & Related papers (2024-08-19T16:42:58Z) - Learning Enriched Features via Selective State Spaces Model for Efficient Image Deblurring [0.0]
Image deblurring aims to restore a high-quality image from its corresponding blurred.
We propose an efficient image deblurring network that leverages selective state spaces model to aggregate enriched and accurate features.
Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches on widely used benchmarks.
arXiv Detail & Related papers (2024-03-29T10:40:41Z) - DGNet: Dynamic Gradient-Guided Network for Water-Related Optics Image
Enhancement [77.0360085530701]
Underwater image enhancement (UIE) is a challenging task due to the complex degradation caused by underwater environments.
Previous methods often idealize the degradation process, and neglect the impact of medium noise and object motion on the distribution of image features.
Our approach utilizes predicted images to dynamically update pseudo-labels, adding a dynamic gradient to optimize the network's gradient space.
arXiv Detail & Related papers (2023-12-12T06:07:21Z) - DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection.
It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor.
Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
arXiv Detail & Related papers (2023-12-11T18:38:28Z) - Diffusion Models Without Attention [110.5623058129782]
Diffusion State Space Model (DiffuSSM) is an architecture that supplants attention mechanisms with a more scalable state space model backbone.
Our focus on FLOP-efficient architectures in diffusion training marks a significant step forward.
arXiv Detail & Related papers (2023-11-30T05:15:35Z) - DPFNet: A Dual-branch Dilated Network with Phase-aware Fourier
Convolution for Low-light Image Enhancement [1.2645663389012574]
Low-light image enhancement is a classical computer vision problem aiming to recover normal-exposure images from low-light images.
convolutional neural networks commonly used in this field are good at sampling low-frequency local structural features in the spatial domain.
We propose a novel module using the Fourier coefficients, which can recover high-quality texture details under the constraint of semantics in the frequency phase.
arXiv Detail & Related papers (2022-09-16T13:56:09Z) - PC-GANs: Progressive Compensation Generative Adversarial Networks for
Pan-sharpening [50.943080184828524]
We propose a novel two-step model for pan-sharpening that sharpens the MS image through the progressive compensation of the spatial and spectral information.
The whole model is composed of triple GANs, and based on the specific architecture, a joint compensation loss function is designed to enable the triple GANs to be trained simultaneously.
arXiv Detail & Related papers (2022-07-29T03:09:21Z) - Spatially-Adaptive Image Restoration using Distortion-Guided Networks [51.89245800461537]
We present a learning-based solution for restoring images suffering from spatially-varying degradations.
We propose SPAIR, a network design that harnesses distortion-localization information and dynamically adjusts to difficult regions in the image.
arXiv Detail & Related papers (2021-08-19T11:02:25Z)
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.