2018/07/09 by Milad Niknejad, José M. Bioucas‐Dias, Niknejad, Milad +5
Computer Science · Engineering · #Advanced Image Fusion Techniques #Advanced Image Processing Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Image and Signal Denoising Methods #Medical Image Segmentation Techniques #cs.CV
paper · pdf · doi:10.48550/arxiv.1807.03027
arxiv created 2018/07/09 · openalex publication_date 2018/07/09 · arxiv updated 2018/07/10 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
This paper proposes a general framework for internal patch-based image restoration based on Conditional Random Fields (CRF). Unlike related models based on Markov Random Fields (MRF), our approach explicitly formulates the posterior distribution for the entire image. The potential functions are taken as proportional to the product of a likelihood and prior for each patch. By assuming identical parameters for similar patches, our approach can be classified as a model-based non-local method. For the prior term in the potential function of the CRF model, multivariate Gaussians and multivariate scale-mixture of Gaussians are considered, with the latter being a novel prior for image patches. Our results show that the proposed approach outperforms methods based on Gaussian mixture models for image denoising and state-of-the-art methods for image interpolation/inpainting.