# INFWIDE: Image and Feature Space Wiener Deconvolution Network for Non-blind Image Deblurring in Low-Light Conditions

arXiv: https://arxiv.org/abs/2207.08201 · HTML: https://mlsanity.com/paper/2207.08201/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 53 references that are in the map.
- First posted on arXiv 17 Jul 2022.

## Builds on (most cited first, 17 of 17)

- [Learning Deep CNN Denoiser Prior for Image Restoration](https://mlsanity.com/paper/1704.03264/) (2017)
- [Deep Unfolding Network for Image Super-Resolution](https://mlsanity.com/paper/2003.10428/) (2020)
- [Unprocessing Images for Learned Raw Denoising](https://mlsanity.com/paper/1811.11127/) (2018)
- [Deep Image Deblurring: A Survey](https://mlsanity.com/paper/2201.10700/) (2022)
- [Reblur2Deblur: Deblurring Videos via Self-Supervised Learning](https://mlsanity.com/paper/1801.05117/) (2018)
- [DWDN: Deep Wiener Deconvolution Network for Non-Blind Image Deblurring](https://mlsanity.com/paper/2103.09962/) (2021)
- [Learning Fully Convolutional Networks for Iterative Non-blind Deconvolution](https://mlsanity.com/paper/1611.06495/) (2016)
- [High-level numerical simulations of noise in CCD and CMOS photosensors: review and tutorial](https://mlsanity.com/paper/1412.4031/) (2014)
- [Learning Deep Gradient Descent Optimization for Image Deconvolution](https://mlsanity.com/paper/1804.03368/) (2018)
- [Clean Images are Hard to Reblur: Exploiting the Ill-Posed Inverse Task for Dynamic Scene Deblurring](https://mlsanity.com/paper/2104.12665/) (2021)
- [End-to-end Interpretable Learning of Non-blind Image Deblurring](https://mlsanity.com/paper/2007.01769/) (2020)
- [Photon Limited Non-Blind Deblurring Using Algorithm Unrolling](https://mlsanity.com/paper/2110.15314/) (2021)
- [Modeling Realistic Degradations in Non-blind Deconvolution](https://mlsanity.com/paper/1806.01097/) (2018)
- [Cascades of Regression Tree Fields for Image Restoration](https://mlsanity.com/paper/1404.2086/) (2014)
- [Efficient Blind Deblurring under High Noise Levels](https://mlsanity.com/paper/1904.09154/) (2019)
- [Handling noise in image deblurring via joint learning](https://mlsanity.com/paper/2001.09730/) (2020)
- [Comparative Analysis of Non-Blind Deblurring Methods for Noisy Blurred Images](https://mlsanity.com/paper/2205.03464/) (2022)

## Built on by (most cited first, 8 of 8)

- [Language-driven All-in-one Adverse Weather Removal](https://mlsanity.com/paper/2312.01381/) (2023)
- [F2former: When Fractional Fourier Meets Deep Wiener Deconvolution and Selective Frequency Transformer for Image Deblurring](https://mlsanity.com/paper/2409.02056/) (2024)
- [Aberration Correcting Vision Transformers for High-Fidelity Metalens Imaging](https://mlsanity.com/paper/2412.04591/) (2024)
- [Lightweight High-Speed Photography Built on Coded Exposure and Implicit Neural Representation of Videos](https://mlsanity.com/paper/2311.13134/) (2023)
- [How to deal with glare for improved perception of Autonomous Vehicles](https://mlsanity.com/paper/2404.10992/) (2024)
- [A review of advancements in low-light image enhancement using deep learning](https://mlsanity.com/paper/2505.05759/) (2025)
- [Saturation-Aware Space-Variant Blind Image Deblurring](https://mlsanity.com/paper/2604.16200/) (2026)
- [Physen-Noise2Noise: Physics-Guided Self-Supervised Defocus Deblurring with Bias Correction under Low-Light Conditions](https://mlsanity.com/paper/2605.24590/) (2026)

## Abstract (arXiv)

Under low-light environment, handheld photography suffers from severe camera shake under long exposure settings. Although existing deblurring algorithms have shown promising performance on well-exposed blurry images, they still cannot cope with low-light snapshots. Sophisticated noise and saturation regions are two dominating challenges in practical low-light deblurring. In this work, we propose a novel non-blind deblurring method dubbed image and feature space Wiener deconvolution network (INFWIDE) to tackle these problems systematically. In terms of algorithm design, INFWIDE proposes a two-branch architecture, which explicitly removes noise and hallucinates saturated regions in the image space and suppresses ringing artifacts in the feature space, and integrates the two complementary outputs with a subtle multi-scale fusion network for high quality night photograph deblurring. For effective network training, we design a set of loss functions integrating a forward imaging model and backward reconstruction to form a close-loop regularization to secure good convergence of the deep neural network. Further, to optimize INFWIDE's applicability in real low-light conditions, a physical-process-based low-light noise model is employed to synthesize realistic noisy night photographs for model training. Taking advantage of the traditional Wiener deconvolution algorithm's physically driven characteristics and arisen deep neural network's representation ability, INFWIDE can recover fine details while suppressing the unpleasant artifacts during deblurring. Extensive experiments on synthetic data and real data demonstrate the superior performance of the proposed approach.