Explore MATLAB source code curated for "超分辨率" with clean implementations, documentation, and examples.
Super-resolution reconstruction program using POCS (Projection onto Convex Sets) method, implementing high-definition image reconstruction through iterative projections and constraints
An image processing super-resolution reconstruction method primarily utilizing sparse representation techniques for image reconstruction, with implementation involving sparse coding algorithms and dictionary learning approaches
Image super-resolution reconstruction algorithms encompass various methods including interpolation, iterative back-projection, MAP (Maximum A Posteriori), POCS (Projection Onto Convex Sets), and registration-based approaches with implementation details
MATLAB implementation of learning-based super-resolution reconstruction with detailed code description, providing valuable resources for super-resolution research
Super-resolution image processing extracts pixels from several images and synthesizes them to generate new, clearer images using advanced computational techniques
Enhanced multi-frame image super-resolution reconstruction algorithm utilizing L1-norm regularization, improving reconstruction accuracy and computational efficiency compared to traditional approaches.
Super-resolution image processing extracts pixels from several source images and synthesizes them into a new, clearer image with enhanced quality.
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.
A super-resolution program developed in MATLAB with direct execution capability, featuring image enhancement through advanced processing techniques
The effectiveness of super-resolution (SR) techniques relies on leveraging slightly different perspectives across multiple low-resolution images containing the same object. This process aggregates complementary information to exceed the data available in any single frame. Optimal performance is achieved when objects exhibit motion in video sequences, enabling motion detection and tracking to multiply benefits through sub-pixel alignment and temporal fusion. If objects remain static across frames, no additional information can be extracted. However, rapid motion or transformation creates distinct appearances across frames, which can be exploited through registration algorithms and motion compensation techniques to reconstruct high-resolution details.