Image Restoration Using Markov Random Fields

This paper introduces an image restoration approach using Markov Random Fields (MRF), providing program implementations for generating MRF models and demonstrating their application in image denoising tasks. MRF serves as a probabilistic graphical model for image modeling that effectively captures relationships and interactions between pixels. The implementation typically involves defining neighborhood systems, establishing energy functions that incorporate both data fidelity terms and smoothness constraints, and optimizing through algorithms like Iterated Conditional Modes (ICM) or Gibbs sampling. For image denoising applications, the MRF model can be configured with appropriate potential functions that balance noise removal with edge preservation. Through our provided code, users can generate customized MRF models and apply them to their own image denoising workflows, effectively improving image quality and enhancing visual experiences by reducing noise while maintaining important image structures.