Data to 7 Oct 2026

NBNet: Noise Basis Learning for Image Denoising with Subspace Projection

arXiv 2012.15028 ↗First posted 30 Dec 2020cs.CVcited by 77

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Abstract (arXiv)

In this paper, we introduce NBNet, a novel framework for image denoising. Unlike previous works, we propose to tackle this challenging problem from a new perspective: noise reduction by image-adaptive projection. Specifically, we propose to train a network that can separate signal and noise by learning a set of reconstruction basis in the feature space. Subsequently, image denosing can be achieved by selecting corresponding basis of the signal subspace and projecting the input into such space. Our key insight is that projection can naturally maintain the local structure of input signal, especially for areas with low light or weak textures. Towards this end, we propose SSA, a non-local subspace attention module designed explicitly to learn the basis generation as well as the subspace projection. We further incorporate SSA with NBNet, a UNet structured network designed for end-to-end image denosing. We conduct evaluations on benchmarks, including SIDD and DND, and NBNet achieves state-of-the-art performance on PSNR and SSIM with significantly less computational cost.

Key facts. Cited by 77 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Dec 2020.

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Citing papers per year: 2021: 5; 2022: 14; 2023: 33; 2024: 19; 2025: 4; 2026: 2.

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2021Uformer: A General U-Shaped Transformer for Image Restorationcited by 864
2022Simple Baselines for Image Restorationcited by 701
2022Deep Generalized Unfolding Networks for Image Restorationcited by 119
2022AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Networkcited by 92
2023Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Modelscited by 86
2021Intriguing Findings of Frequency Selection for Image Deblurringcited by 80
2023Masked Image Training for Generalizable Deep Image Denoisingcited by 57
2021Adaptive Unfolding Total Variation Network for Low-Light Image Enhancementcited by 55
2023Prompt-In-Prompt Learning for Universal Image Restorationcited by 43
2024MaIR: A Locality- and Continuity-Preserving Mamba for Image Restorationcited by 43
2023Xformer: Hybrid X-Shaped Transformer for Image Denoisingcited by 37
2023LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoisingcited by 37

38 more

2023Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoisingcited by 35
2023KBNet: Kernel Basis Network for Image Restorationcited by 33
2023Spatially Adaptive Self-Supervised Learning for Real-World Image Denoisingcited by 33
2023Random Sub-Samples Generation for Self-Supervised Real Image Denoisingcited by 31
2023Unsupervised Image Denoising in Real-World Scenarios via Self-Collaboration Parallel Generative Adversarial Branchescited by 29
2023Diffusion Model for Generative Image Denoisingcited by 27
2023Skip-Attention: Improving Vision Transformers by Paying Less Attentioncited by 23
2023DarkVisionNet: Low-Light Imaging via RGB-NIR Fusion with Deep Inconsistency Priorcited by 19
2023Survey on Deep Face Restoration: From Non-blind to Blind and Beyondcited by 18
2023Exploring Efficient Asymmetric Blind-Spots for Self-Supervised Denoising in Real-World Scenarioscited by 17
2023Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensemblingcited by 17
2024CascadedGaze: Efficiency in Global Context Extraction for Image Restorationcited by 16
2022D2HNet: Joint Denoising and Deblurring with Hierarchical Network for Robust Night Image Restorationcited by 14
2023Mixed Hierarchy Network for Image Restorationcited by 14
2024Enhancing RAW-to-sRGB with Decoupled Style Structure in Fourier Domaincited by 14
2024Taming Diffusion Models for Image Restoration: A Reviewcited by 12
2024DemosaicFormer: Coarse-to-Fine Demosaicing Network for HybridEVS Cameracited by 11
2024DualDn: Dual-domain Denoising via Differentiable ISPcited by 11
2023GyroFlow+: Gyroscope-Guided Unsupervised Deep Homography and Optical Flow Learningcited by 10
2023Real-World Denoising via Diffusion Modelcited by 10
2023Learning Physics-Informed Noise Models from Dark Frames for Low-Light Raw Image Denoisingcited by 10
2024Development of Skip Connection in Deep Neural Networks for Computer Vision and Medical Image Analysis: A Surveycited by 10
2024Rethinking Transformer-Based Blind-Spot Network for Self-Supervised Image Denoisingcited by 9
2024Robust Image Semantic Coding with Learnable CSI Fusion Masking over MIMO Fading Channelscited by 9
2023Free Lunch for Gait Recognition: A Novel Relation Descriptorcited by 8
2023A Comparison of Image Denoising Methodscited by 7
2024Re-boosting Self-Collaboration Parallel Prompt GAN for Unsupervised Image Restorationcited by 7
2022Fast and High-Quality Image Denoising via Malleable Convolutionscited by 6
2022Optimizing Image Compression via Joint Learning with Denoisingcited by 6
2023A cross Transformer for image denoisingcited by 6
2024Temporal As a Plugin: Unsupervised Video Denoising with Pre-Trained Image Denoiserscited by 6
2022ELMformer: Efficient Raw Image Restoration with a Locally Multiplicative Transformercited by 5
2023MIPI 2023 Challenge on RGBW Remosaic: Methods and Resultscited by 5
2023Realistic Noise Synthesis with Diffusion Modelscited by 5
2024Complementary Advantages: Exploiting Cross-Field Frequency Correlation for NIR-Assisted Image Denoisingcited by 5
2022Robust Deep Ensemble Method for Real-world Image Denoisingcited by 4
2022Coarse-to-Fine Video Denoising with Dual-Stage Spatial-Channel Transformercited by 3
2024PanAdapter: Two-Stage Fine-Tuning with Spatial-Spectral Priors Injecting for Pansharpeningcited by 3

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