Applied aspects of modern non-blind image deconvolution methods

The focus of this paper is the study of modern non-blind image deconvolution methods and their application to practical tasks. The aim of the study is to determine the current state-of-the-art in non-blind image deconvolution and to identify the limitations of current approaches, with a focus on practical application details. The paper proposes approaches to examine the influence of various effects on the quality of restoration, the robustness of models to errors in blur kernel estimation, and the violation of the commonly assumed uniform blur model. We developed a benchmark for validating non-blind deconvolution methods, which includes datasets of ground truth images and blur kernels, as well as a test scheme for assessing restoration quality and error robustness. Our experimental results show that those neural network models lacking any pre-optimization, such as quantization or knowledge distillation, fall short of classical methods in several key properties, such as inference speed or the ability to handle different types of blur. Nevertheless, neural network models have made notable progress in their robustness to noise and distortions. Based on the results of the study, we provided recommendations for more effective use of modern non-blind image deconvolution methods. We also developed suggestions for improving the robustness, versatility and performance quality of the models by incorporating additional practices into the training pipeline.