Image Restoration Using Markov Random Fields

In this article, we explore image restoration techniques based on Markov Random Fields (MRFs). We introduce a program for generating MRFs and demonstrate its application in image denoising. This process is crucial as digital image processing technologies become increasingly important with the widespread use of digital images. We provide detailed explanations of the implementation approach, including key algorithms such as the Gibbs distribution sampling and neighborhood system configuration. The article covers practical coding considerations for energy function minimization using techniques like Iterated Conditional Modes (ICM) or Graph Cuts. We also discuss fundamental concepts including noise models and the underlying principles of image restoration to help readers gain deeper understanding of the subject. Through this study, readers will master MRF-based image restoration methods and be able to apply these techniques to solve practical problems, with specific attention to parameter tuning and performance optimization in real-world scenarios.