Explore MATLAB source code curated for "超分辨率复原" with clean implementations, documentation, and examples.
Super-Resolution Reconstruction (Divided into 1024 Patches) involves partitioning high-resolution and low-resolution images from the sample library into patches. When a new low-resolution image is input, it is divided into small patches to search for the best matching high-resolution patches in the sample library, which are then used to reconstruct the high-resolution image.
A fast, sub-pixel three-step algorithm for motion matrix estimation in super-resolution reconstruction using block matching techniques, with enhanced computational efficiency and precision.
Super-resolution restoration (block-based processing) involves dividing high-resolution and low-resolution images from a sample library into blocks. When a new low-resolution image is input, it is segmented into small blocks to find the best matching high-resolution blocks in the sample library, which are then used to reconstruct the high-resolution image.