Explore MATLAB source code curated for "图像重建" with clean implementations, documentation, and examples.
An image reconstruction program leveraging compressive sensing technology to enhance both reconstruction quality and processing speed, with implementation details including sparse representation and optimization algorithms
Contains images with code implementation. The code is straightforward, primarily utilizing principal component analysis with key functions for dimensionality reduction and data reconstruction.
Implementation of CT Algebraic Reconstruction Technique (ART) for tomographic image reconstruction using MATLAB with mathematical modeling and iterative optimization approaches
Image reconstruction based on the KSVD algorithm, including sample images, implementation code, and learned dictionaries for reference
Enhanced multi-frame image super-resolution reconstruction algorithm utilizing L1-norm regularization, improving reconstruction accuracy and computational efficiency compared to traditional approaches.
This is a sparse representation-based image reconstruction algorithm designed for image deblurring and super-resolution applications, featuring practical implementation with code examples demonstrating dictionary learning and sparse coding techniques.
Original implementation code by Jianchao Yang for single image super-resolution using sparse representation. The algorithm works by first partitioning high/low-resolution training images into patches, then training patch pairs into coupled dictionaries. Testing images are mapped to the low-resolution dictionary to obtain sparse coefficients, which are then multiplied with the high-resolution dictionary to reconstruct the final image. This implementation provides excellent reference material for students studying super-resolution algorithms and demonstrates practical dictionary learning applications.
OS-EM Iterative Algorithm for Image Reconstruction - A novel approach that effectively reduces artifacts using ordered subsets and expectation-maximization optimization
An enhanced super-resolution image reconstruction algorithm using Projection onto Convex Sets (POCS) methodology, featuring improved computational accuracy and efficiency through advanced optimization techniques and algorithmic refinements.
MATLAB-based implementation of image reconstruction using fan-beam filtered backprojection method with detailed code descriptions and algorithm explanations