Exploring Richer and More Accurate Information via Frequency Selection for Image Restoration
- URL: http://arxiv.org/abs/2407.08950v1
- Date: Fri, 12 Jul 2024 03:10:08 GMT
- Title: Exploring Richer and More Accurate Information via Frequency Selection for Image Restoration
- Authors: Hu Gao, Depeng Dang,
- Abstract summary: We introduce a multi-scale frequency selection network (MSFSNet) that seamlessly integrates spatial and frequency domain knowledge.
Our MSFSNet achieves performance that is either superior or comparable to state-of-the-art algorithms.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Image restoration aims to recover high-quality images from their corrupted counterparts. Many existing methods primarily focus on the spatial domain, neglecting the understanding of frequency variations and ignoring the impact of implicit noise in skip connections. In this paper, we introduce a multi-scale frequency selection network (MSFSNet) that seamlessly integrates spatial and frequency domain knowledge, selectively recovering richer and more accurate information. Specifically, we initially capture spatial features and input them into dynamic filter selection modules (DFS) at different scales to integrate frequency knowledge. DFS utilizes learnable filters to generate high and low-frequency information and employs a frequency cross-attention mechanism (FCAM) to determine the most information to recover. To learn a multi-scale and accurate set of hybrid features, we develop a skip feature fusion block (SFF) that leverages contextual features to discriminatively determine which information should be propagated in skip-connections. It is worth noting that our DFS and SFF are generic plug-in modules that can be directly employed in existing networks without any adjustments, leading to performance improvements. Extensive experiments across various image restoration tasks demonstrate that our MSFSNet achieves performance that is either superior or comparable to state-of-the-art algorithms.
Related papers
- Spatial and Frequency Domain Adaptive Fusion Network for Image Deblurring [0.0]
Image deblurring aims to reconstruct a latent sharp image from its corresponding blurred one.
We propose a spatial-frequency domain adaptive fusion network (SFAFNet) to address this limitation.
Our SFAFNet performs favorably compared to state-of-the-art approaches on commonly used benchmarks.
arXiv Detail & Related papers (2025-02-20T02:43:55Z) - Wavelet-Assisted Multi-Frequency Attention Network for Pansharpening [15.77836708727337]
Pansharpening aims to combine a high-resolution panchromatic (PAN) image with a low-resolution multispectral (LRMS) image to produce a high-resolution multispectral (HRMS) image.
Although pansharpening in the frequency domain offers clear advantages, most existing methods either continue to operate solely in the spatial domain or fail to fully exploit the benefits of the frequency domain.
We propose Multi-Frequency Fusion Attention (MFFA), which leverages wavelet transforms to cleanly separate frequencies.
arXiv Detail & Related papers (2025-02-07T13:15:49Z) - 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) - Mutual-Guided Dynamic Network for Image Fusion [51.615598671899335]
We propose a novel mutual-guided dynamic network (MGDN) for image fusion, which allows for effective information utilization across different locations and inputs.
Experimental results on five benchmark datasets demonstrate that our proposed method outperforms existing methods on four image fusion tasks.
arXiv Detail & Related papers (2023-08-24T03:50:37Z) - Both Spatial and Frequency Cues Contribute to High-Fidelity Image
Inpainting [9.080472817672263]
Deep generative approaches have obtained great success in image inpainting recently.
Most generative inpainting networks suffer from either over-smooth results or aliasing artifacts.
We propose an effective Frequency-Spatial Complementary Network (FSCN) by exploiting rich semantic information in both spatial and frequency domains.
arXiv Detail & Related papers (2023-07-15T01:52:06Z) - Complementary Frequency-Varying Awareness Network for Open-Set
Fine-Grained Image Recognition [14.450381668547259]
Open-set image recognition is a challenging topic in computer vision.
We propose a Complementary Frequency-varying Awareness Network that could better capture both high-frequency and low-frequency information.
Based on CFAN, we propose an open-set fine-grained image recognition method, called CFAN-OSFGR.
arXiv Detail & Related papers (2023-07-14T08:15:36Z) - Multi-scale frequency separation network for image deblurring [10.511076996096117]
We present a new method called multi-scale frequency separation network (MSFS-Net) for image deblurring.
MSFS-Net captures the low and high-frequency information of image at multiple scales.
Experiments on benchmark datasets show that the proposed network achieves state-of-the-art performance.
arXiv Detail & Related papers (2022-06-01T23:48:35Z) - 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) - Deep Frequency Filtering for Domain Generalization [55.66498461438285]
Deep Neural Networks (DNNs) have preferences for some frequency components in the learning process.
We propose Deep Frequency Filtering (DFF) for learning domain-generalizable features.
We show that applying our proposed DFF on a plain baseline outperforms the state-of-the-art methods on different domain generalization tasks.
arXiv Detail & Related papers (2022-03-23T05:19:06Z) - TBNet:Two-Stream Boundary-aware Network for Generic Image Manipulation
Localization [49.521622399483846]
We propose a novel end-to-end two-stream boundary-aware network (abbreviated as TBNet) for generic image manipulation localization.
The proposed TBNet can significantly outperform state-of-the-art generic image manipulation localization methods in terms of both MCC and F1.
arXiv Detail & Related papers (2021-08-10T08:22:05Z) - Wavelet-Based Network For High Dynamic Range Imaging [64.66969585951207]
Existing methods, such as optical flow based and end-to-end deep learning based solutions, are error-prone either in detail restoration or ghosting artifacts removal.
In this work, we propose a novel frequency-guided end-to-end deep neural network (FNet) to conduct HDR fusion in the frequency domain, and Wavelet Transform (DWT) is used to decompose inputs into different frequency bands.
The low-frequency signals are used to avoid specific ghosting artifacts, while the high-frequency signals are used for preserving details.
arXiv Detail & Related papers (2021-08-03T12:26:33Z)
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.