Research on Image Reconstruction Algorithms

This article presents detailed operational procedures for studying image reconstruction algorithms, serving as a learning resource for research and development. The content enables readers to further refine and explore these methods to improve algorithmic effectiveness and expand application domains. Key implementation aspects include iterative reconstruction techniques (e.g., Filtered Back Projection for CT imaging), regularization methods for noise reduction, and optimization algorithms like gradient descent for parameter tuning. The article also incorporates the latest research findings and developmental trends in related fields, helping readers stay updated with cutting-edge advancements. Additionally, it discusses practical code implementation considerations such as handling matrix operations for projection data, implementing interpolation methods for grid reconstruction, and managing computational efficiency through parallel processing techniques. In summary, this work provides a comprehensive and in-depth introductory guide to image reconstruction algorithm research, complete with technical insights for effective implementation.