Papers › Nonblind image deconvolution via leveraging model uncertainty in an untrained deep...
Nonblind image deconvolution via leveraging model uncertainty in an untrained deep neural network
Mingqin Chen; Yuhui Quan; Tongyao Pang; Hui Ji
Nonblind image deconvolution (NID) is about restoring the latent image with sharp details from a noisy blurred one using a known blur kernel. This paper presents a dataset-free deep learning approach for NID using untrained deep neural networks (DNNs), which does not require any external training data with ground-truth images. Based on a spatially-adaptive dropout scheme, the proposed approach learns a DNN with model uncertainty from the input blurred image, and the deconvolution result is obtained by aggregating the multiple predictions from the learned dropout DNN. It is shown that the solution approximates a minimum-mean-squared-error estimator in Bayesian inference. In addition, a self-supervised loss function for training is presented to efficiently handle the noise in blurred images. Extensive experiments show that the proposed approach not only performs noticeably better than existing non-learning-based methods and unsupervised learning-based methods, but also performs competitively against recent supervised learning-based methods.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections