Image Denoising

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7 papers in the last four weeks, down 22% on the four weeks before. 0.1% of all new papers.

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Hybrid++: The Bridge between PDE Models and Deep Learning for Gamma Noise Removal
Oct 6, 2026Mahipal Jetta, Sujato DuttaImage Denoising Physics-Guided Image Restoration
Mahindra University
A BEMD-Based Quaternion Filtering Approach Sharp-to-Soft Kernel CT Image Conversion
Oct 5, 2026Mahmoud Nasr, Jan K. Argasinski, Krzysztof Brzostowski +1Computed Tomography Image Denoising
Sano Centre for Computational Medicine, Czarnowiejska 36/C5, Kraków, 30-054, Poland · Department of Biocybernetics and Biomedical Engineering, AGH University of Krakow, 30-059 Krakow, Poland · Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Łojasiewicza 11, Kraków, 30-348, Poland +1
Decomposition-Guided Curvelet Thresholding for Sharp-to-Soft CT Kernel Conversion
Oct 5, 2026Mahmoud Nasr, Jan K. Argasinski, Krzysztof Brzostowski +1Computed Tomography Image Denoising
Department of Biocybernetics and Biomedical Engineering, AGH University of Krakow, 30-059 Krakow, Poland. · Sano Centre for Computational Medicine, Czarnowiejska 36/C5, Kraków, 30-054, Poland. · Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Łojasiewicza 11, Kraków, 30-348, Poland. +1
Gauss-Map Variation for Image Denoising: Geometric Analysis and an Anderson--Accelerated Majorization--Minimization Method
Oct 5, 2026Haibin SuImage Denoising
Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China
BMND: Direct Poisson Denoising by N-Dimensional Block Matching and Collaborative Filtering
Sep 28, 2026Christof Duhme, Lars Schiefelbein, Florian Büther +1Image Denoising
University of Münster
In-Token Learning for High-Fidelity Image Restoration via Diffusion Transformers
Sep 27, 2026Xingfu Yi, Xiaoxue YuImage-to-Image Translation Diffusion Transformer
Independent Researcher, Hangzhou, China · Zhejiang University, Hangzhou, China
A Study of the Limits of Collaborative DCT-Based Image Denoising via Interpretable Neural Networks
Sep 24, 2026Cristian Comellas, Julia Navarro, Antoni BuadesImage Denoising
Department of Mathematics and Computer Science, Universitat de les Illes Balears, Cra. de Valldemossa, km 7.5, Palma, 07122, Balearic Islands, Spain.
Noise2Noise Revisited: Training Pair Distributions Dominate Loss Choice in Self-Supervised Denoising
Sep 15, 2026Dingyan Shang, Zhenyu Xu, Youting Wang +2Self-Supervised Image Denoising Denoising Autoencoders
Independent Researcher · Frisco, USA · Fulshear, USA +3
Efficient 3D Whole-Body PET Image Denoising via Conditional Rectified Flow With Optimized Sampling Strategy
Sep 15, 2026Jiale Shen, Guolin Wang, Chenhao Wang +3Rectified Flow 3D Medical Imaging
College of Biomedical Engineering & Instrument Science, Zhejiang University, Zhejiang, 310063, China
AXS-Net: Interpretable Deep Unfolding for Hyperspectral Image Denoising via Spectral Basis Unmixing and Structured Noise Refinement
Sep 8, 2026Ziyi Guan, Jianping Zhang, Zheng YangHyperspectral Imaging Deep Unfolding
Xiangtan University, Xiangtan, Hunan, China
Poisson Image Denoising Using Minimax Concave and Reweighted $\ell_1$ Penalties: Nonblind and Blind Approaches
Sep 7, 2026Reza ParvazImage Deblurring Nonconvex Optimization
aDepartment of Engineering Sciences, Faculty of Advanced Technologies,Sep University of Mohaghegh Ardabili, Ardabil, Iran
Benchmarking RAW and RGB Restoration in Image Signal Processors
Sep 2, 2026Zihao Lu, Radu Timofte, Marcos V. CondeImage Restoration Image Denoising
Computer Vision Laboratory CAIDAS and IFI University of Würzburg Würzburg, Germany
Null-Space Diffusion Restoration with Adaptive Uncertainty-Guided Fusion for Ultrasound Speckle Reduction
Aug 30, 2026Juneyong Lee, Jaeyoung ChoiDiffusion Models for Image Restoration Ultrasound Imaging
Department of Computer Science and Engineering, Hankuk University of Foreign Studies, Yongin-si 17035, Republic of Korea
A Globally Convergent Algorithm for Total Scaled-Gradient Variation via Cone-Constrained Bilinear Decomposition
Aug 29, 2026Haibin Su, Chunlin Wu, Huibin Chang +1Image Reconstruction Nonconvex Optimization
Yau Mathematical Sciences Center, Tsinghua University, Beijing 100084, China · School of Mathematical Sciences, Nankai University, Tianjin, 300071, China · School of Mathematical Sciences and Institute of Mathematics and Interdisciplinary Sciences, Tianjin Normal University, Tianjin, 300387, China
Fidelity-Constrained Anchoring for Black-Box Denoisers
Aug 13, 2026Masaki SatohImage Restoration Image Denoising
Morpho, Inc. Tokyo, Japan
Hybrid-LUT: Channel-Aware Hybrid Lookup Table and Filtering for Efficient Image Denoising
Aug 12, 2026Zhilin Ai, Boyu Li, Sidi Yang +5Image Denoising
The University of Hong Kong
New Orthogonal Multiwavelet Filters Derived by Matrix Spectral Factorization
Aug 12, 2026Vasil Kolev, Todor Cooklev, Fritz KeinertWavelet Methods Image Denoising
Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Bl. 2, Acad. G. Bonchev St., 1113 Sofia, Bulgaria · Wireless Technology Center, Purdue University, Fort Wayne, IN 46805, USA · Department of Mathematics, Iowa State University, Ames, IA 50011, USA
Hardware-Aware Deployment of Joint SAR Compression and Despeckling on FPGA
Aug 11, 2026Cédric Léonard, Francescopaolo Sica, Martin SchulzLearned Image Compression Edge Computing
Technical University of Munich, Munich, Germany · Remote Sensing Technology Institute, German Aerospace Center (DLR), Weßling, Germany · Department of Aerospace Engineering, University of the Bundeswehr Munich, Neubiberg, Germany
ENCORE: Efficient Noise Context-Aware Representation for Low-Dose CT Denoising
Aug 11, 2026Minwoo Yu, N. Robert Bennett, Jongduk Baek +1Dynamic Convolution Computed Tomography
Department of Artificial Intelligence, Yonsei University, South Korea · Department of Radiology, Stanford University, Stanford, CA 94305 USA · Department of Radiology and the Department of Electrical Engineering, Stanford University, Stanford, CA 94305 USA
DocPure: Prompt-Free Unified Document Restoration via Degradation-Aware Structure-Guided Wavelet Modulation
Aug 10, 2026Lingming Su, Wanglong Lu, Tao Wang +4Image Deblurring All-in-One Image Restoration
College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, China · College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China · AI Analytics Team, Nasdaq, St. John's, Canada +2
Structural Guidance for Unified Joint Demosaicing and Denoising
Aug 7, 2026Qixin Zheng, Ping Chen, Qiangqiang Shen +1Computational Imaging Image Restoration
Harbin Institute of Technology (Shenzhen), Shenzhen, China
Implicit Neural Speckle Denoising
Aug 6, 2026Matthew R. Ziemann, Casey J. Pellizzari, Tyler J. Hardy +1Dynamic Scene Reconstruction Computational Imaging
DEVCOM Army Research Laboratory, 2800 Powder Mill Rd, Adelphi, MD, USA · Department of Computer Science, University of Maryland, 8125 Paint Branch Dr, College Park, MD, USA · Department of Physics and Meteorology, United States Air Force Academy, USAFA, CO, USA
LiteKD-Net: Lightweight Knowledge-Distilled Network for Mobile Image Denoising
Aug 6, 2026Zhou ZhiyiLightweight CNNs On-Device Inference
Global College Shanghai Jiaotong University Shanghai, China
A geometry-based deep equilibrium model for image restoration under multiplicative Gamma noise
Aug 5, 2026Shengkun Yang, Luca Ratti, Zhichang GuoImage Deblurring Image Restoration
School of Mathematics, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China. · Department of Mathematics, University of Bologna, Bologna, 40126, Emilia-Romagna, Italy.
When Extreme Darkness Meets Motion Blur: MeanFlow for Unified RAW Restoration
Aug 3, 2026Zepu Wang, Jingze Liang, Weijie Xiao +1Low-Light Image Enhancement Image Denoising
University of Electronic Science and Technology of China
Practical Noise Modeling for SPAD Intensity Imaging
Aug 1, 2026Wendi Liu, Yujie Lu, Zengxi Zhang +3Computational Imaging Image Denoising
The University of Tokyo
LoTA-N2N: Local Trace Adaptation for Zero-Shot Self-Supervised Image Denoising
Jul 27, 2026Jintong Hu, Bin Xia, Junlin Liu +2Self-Supervised Image Denoising Self-Supervised Learning
Tsinghua University · The Chinese University of Hong Kong · University of Southern California +1
Trainable Nonexpansive Denoisers for Contractive Image Reconstruction
Jul 25, 2026Arghya Sinha, Aditya Banerjee, Trishit Mukherjee +1Image Reconstruction Lipschitz Neural Networks
Indian Institute of Science, Bengaluru, India.
Small, Bias-Free, Blind and Convolutional Denoiser: A compact ConvNeXt U-Net for blind Gaussian color-image denoising
Jul 24, 2026Nikolas MarkouU-Net Convolutional Neural Networks
$γ$ -Bridge: A Look-Parametric Diffusion Bridge
Jul 21, 2026Xuran Hu, Yujie Zhu, Tengxi Wang +2Diffusion Models for Image Restoration Physics-Guided Image Restoration
Urban Governance and Design Thrust, Society Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · Faculty of Science and Engineering, Macquarie University, NSW 2109, Sydney, Australia · School of Geography and Planning, Ningxia University, Yinchuan, China

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Hybrid++: The Bridge between PDE Models and Deep Learning for Gamma Noise Removal

Mahipal Jetta, Sujato Dutta

Mahindra University

Image Denoising Physics-Guided Image Restoration

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Multiplicative gamma noise is one of the dominant noise factors in Synthetic Aperture Radar (SAR) and medical ultrasound images. They are dependent on pixel level noises due to which they are highly varying across the image and harder to handle as compared to additive noise. The denoising methods to address this noise currently include classical partial differential equation (PDE) methods which are good for interpretation but lack the restoration ability as compared to the state-of-the-art models while deep convolutional networks like DnCNN achieve high performance but at the cost of transparency due to which practitioners are skeptical to use them in high-risk critical fields like medicine. This paper presents Hybrid++, a novel trainable nonlinear reaction-diffusion architecture that addresses both the concerns - staying interpretable while offering performance close to huge black-box models. It combines a fully learnable PDE initialization with a 3-stage reaction-diffusion network having 64-channel multiscale filter banks, 4-layer Squeeze-and-Excitation attention-based influence functions and a 64-dimensional noise level embedding. It uses a two-phase training strategy, stage-wise optimization followed by joint end-to-end refinement which enables co-adaptation of all learnable parameters. On the FoE benchmark, Hybrid++ substantially improves over classical PDE, BM3D and the original TNRD baselines. In the severe-noise setting L=1, it comes within 0.23 dB PSNR of a separately trained DnCNN while using only about 8% of its parameters. We therefore position Hybrid++ not as a universal state-of-the-art image restoration backbone, but as a compact, physically structured reaction-diffusion model for multiplicative gamma noise.

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