# Modeling Realistic Degradations in Non-blind Deconvolution

arXiv: https://arxiv.org/abs/1806.01097 · HTML: https://mlsanity.com/paper/1806.01097/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2018.

## Built on by (most cited first, 6 of 6)

- [DWDN: Deep Wiener Deconvolution Network for Non-Blind Image Deblurring](https://mlsanity.com/paper/2103.09962/) (2021)
- [INFWIDE: Image and Feature Space Wiener Deconvolution Network for Non-blind Image Deblurring in Low-Light Conditions](https://mlsanity.com/paper/2207.08201/) (2022)
- [End-to-end Learning for Joint Depth and Image Reconstruction from Diffracted Rotation](https://mlsanity.com/paper/2204.07076/) (2022)
- [Iterative Reweighted Least Squares Networks With Convergence Guarantees for Solving Inverse Imaging Problems](https://mlsanity.com/paper/2308.05745/) (2023)
- [Double Blind Imaging with Generative Modeling](https://mlsanity.com/paper/2503.21501/) (2025)
- [Assessing the Role of Datasets in the Generalization of Motion Deblurring Methods to Real Images](https://mlsanity.com/paper/2209.12675/) (2022)

## Abstract (arXiv)

Most image deblurring methods assume an over-simplistic image formation model and as a result are sensitive to more realistic image degradations. We propose a novel variational framework, that explicitly handles pixel saturation, noise, quantization, as well as non-linear camera response function due to e.g., gamma correction. We show that accurately modeling a more realistic image acquisition pipeline leads to significant improvements, both in terms of image quality and PSNR. Furthermore, we show that incorporating the non-linear response in both the data and the regularization terms of the proposed energy leads to a more detailed restoration than a naive inversion of the non-linear curve. The minimization of the proposed energy is performed using stochastic optimization. A dataset consisting of realistically degraded images is created in order to evaluate the method.