2015/03/19 by Yunjin Chen, Wei Yu, Chen, Yunjin +3 · 1 citation
Computer Science · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image and Signal Denoising Methods #Medical Image Segmentation Techniques #Medical Imaging Techniques and Applications #cs.CV
paper · pdf · doi:10.48550/arxiv.1503.05768
9 pages, 3 figures, 3 tables. CVPR2015 oral presentation together with the supplemental material of 13 pages, 8 pages (Notes on diffusion networks)
openalex publication_date 2015/03/19 · arxiv created 2015/03/25 · arxiv updated 2015/03/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
For several decades, image restoration remains an active research topic in low-level computer vision and hence new approaches are constantly emerging. However, many recently proposed algorithms achieve state-of-the-art performance only at the expense of very high computation time, which clearly limits their practical relevance. In this work, we propose a simple but effective approach with both high computational efficiency and high restoration quality. We extend conventional nonlinear reaction diffusion models by several parametrized linear filters as well as several parametrized influence functions. We propose to train the parameters of the filters and the influence functions through a loss based approach. Experiments show that our trained nonlinear reaction diffusion models largely benefit from the training of the parameters and finally lead to the best reported performance on common test datasets for image restoration. Due to their structural simplicity, our trained models are highly efficient and are also well-suited for parallel computation on GPUs.