Super-Resolution Restoration with Block-Based Processing

MATLAB 3K 243 views 0 downloads 1 credits
Tags:

Resource Overview

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.

Detailed Documentation

In super-resolution restoration with block-based processing, both high-resolution and low-resolution images from the sample library are divided into blocks. When a new low-resolution image is input, it is segmented into small patches. Each patch is then matched against the sample library to identify the most corresponding high-resolution block. These matched high-resolution blocks are subsequently utilized to reconstruct the final high-resolution image. This method enhances image clarity and detail preservation, with applications spanning various domains such as image processing and computer vision. Implementation typically involves key steps like block extraction using sliding window techniques, similarity measurement through algorithms like Sum of Squared Differences (SSD) or Normalized Cross-Correlation (NCC), and seamless block fusion using weighted averaging or Poisson blending to minimize boundary artifacts.

You May Also Like