Efficient RAW Image Deblurring with Adaptive Frequency Modulation

• URL: http://arxiv.org/abs/2505.24407v2
• Date: Tue, 03 Jun 2025 06:52:22 GMT
• Title: Efficient RAW Image Deblurring with Adaptive Frequency Modulation
• Authors: Wenlong Jiao, Binglong Li, Wei Shang, Ping Wang, Dongwei Ren,
• Abstract summary: Deblurring RAW images presents unique challenges, particularly in handling frequency-dependent blur.<n>We propose Frequency Enhanced Network (FrENet), a framework specifically designed for RAW-to-RAW deblurring.<n> Experimental results demonstrate that FrENet surpasses state-of-the-art deblurring methods in RAW image deblurring.
• Score: 11.866039197666808
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image deblurring plays a crucial role in enhancing visual clarity across various applications. Although most deep learning approaches primarily focus on sRGB images, which inherently lose critical information during the image signal processing pipeline, RAW images, being unprocessed and linear, possess superior restoration potential but remain underexplored. Deblurring RAW images presents unique challenges, particularly in handling frequency-dependent blur while maintaining computational efficiency. To address these issues, we propose Frequency Enhanced Network (FrENet), a framework specifically designed for RAW-to-RAW deblurring that operates directly in the frequency domain. We introduce a novel Adaptive Frequency Positional Modulation module, which dynamically adjusts frequency components according to their spectral positions, thereby enabling precise control over the deblurring process. Additionally, frequency domain skip connections are adopted to further preserve high-frequency details. Experimental results demonstrate that FrENet surpasses state-of-the-art deblurring methods in RAW image deblurring, achieving significantly better restoration quality while maintaining high efficiency in terms of reduced MACs. Furthermore, FrENet's adaptability enables it to be extended to sRGB images, where it delivers comparable or superior performance compared to methods specifically designed for sRGB data. The code will be available at https://github.com/WenlongJiao/FrENet .

Related papers

• Freqformer: Image-Demoiréing Transformer via Efficient Frequency Decomposition [83.40450475728792]
  We present Freqformer, a Transformer-based framework specifically designed for image demoir'eing through targeted frequency separation.<n>Our method performs an effective frequency decomposition that explicitly splits moir'e patterns into high-frequency spatially-localized textures and low-frequency scale-robust color distortions.<n>Experiments on various demoir'eing benchmarks demonstrate that Freqformer achieves state-of-the-art performance with a compact model size.
  arXiv  Detail & Related papers  (2025-05-25T12:23:10Z)
• Frequency Enhancement for Image Demosaicking [40.76899837631637]
  We propose Dual-path Frequency Enhancement Network (DFENet), which reconstructs RGB images in a divide-and-conquer manner.<n>One path focuses on generating missing information through detail refinement in spatial domain, while the other aims at suppressing undesirable frequencies.<n>With these designs, the proposed DFENet outperforms other state-of-the-art algorithms on different datasets.
  arXiv  Detail & Related papers  (2025-03-20T02:37:10Z)
• Frequency-Adaptive Low-Latency Object Detection Using Events and Frames [23.786369609995013]
  Fusing Events and RGB images for object detection leverages the robustness of Event cameras in adverse environments.<n>Two critical mismatches: low-latency Events textitvs.high-latency RGB frames, and temporally sparse labels in training textitvs.continuous flow in inference.<n>We propose the textbfFrequency-textbfAdaptive Low-Latency textbfObject textbfDetector (FAOD)
  arXiv  Detail & Related papers  (2024-12-05T13:23:06Z)
• Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image Enhancement [71.13353154514418]
  Low-light image enhancement, particularly in cross-domain tasks such as mapping from the raw domain to the sRGB domain, remains a significant challenge.<n>We present a novel Mamba scanning mechanism, called RAWMamba, to effectively handle raw images with different CFAs.<n>We also present a Retinex Decomposition Module (RDM) grounded in Retinex prior, which decouples illumination from reflectance to facilitate more effective denoising and automatic non-linear exposure correction.
  arXiv  Detail & Related papers  (2024-09-11T06:12:03Z)
• A Learnable Color Correction Matrix for RAW Reconstruction [19.394856071610604]
  We introduce a learnable color correction matrix (CCM) to approximate the complex inverse image signal processor (ISP) Experimental results demonstrate that simulated RAW (simRAW) images generated by our method provide performance improvements equivalent to those produced by more complex inverse ISP methods.
  arXiv  Detail & Related papers  (2024-09-04T07:46:42Z)
• DEFormer: DCT-driven Enhancement Transformer for Low-light Image and Dark Vision [12.150160523389957]
  We propose a DCT-driven enhancement transformer (DEFormer) framework to restore lost details in the dark area.<n>Our framework has achieved superior results on the LOL and MIT-Adobe FiveK datasets.
  arXiv  Detail & Related papers  (2023-09-13T13:24:27Z)
• Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision [76.41657124981549]
  This paper presents a joint learning model for image alignment and RAW-to-sRGB mapping. Experiments show that our method performs favorably against state-of-the-arts on ZRR and SR-RAW datasets.
  arXiv  Detail & Related papers  (2021-08-18T12:41:36Z)
• 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)
• Deep Burst Super-Resolution [165.90445859851448]
  We propose a novel architecture for the burst super-resolution task. Our network takes multiple noisy RAW images as input, and generates a denoised, super-resolved RGB image as output. In order to enable training and evaluation on real-world data, we additionally introduce the BurstSR dataset.
  arXiv  Detail & Related papers  (2021-01-26T18:57:21Z)
• Frequency Consistent Adaptation for Real World Super Resolution [64.91914552787668]
  We propose a novel Frequency Consistent Adaptation (FCA) that ensures the frequency domain consistency when applying Super-Resolution (SR) methods to the real scene. We estimate degradation kernels from unsupervised images and generate the corresponding Low-Resolution (LR) images. Based on the domain-consistent LR-HR pairs, we train easy-implemented Convolutional Neural Network (CNN) SR models.
  arXiv  Detail & Related papers  (2020-12-18T08:25:39Z)

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.