MATLAB Implementation of Image Registration with Dual-Criterion Approach

In this implementation, we first perform basic image preprocessing operations to ensure registration accuracy. The MATLAB code typically includes steps like noise reduction using imgaussfilt() or medfilt2() functions, contrast enhancement through histeq(), and image resizing via imresize() to standardize input dimensions. Subsequently, image registration is performed based on two distinct criteria: mutual information and edge-interactive variance. For mutual information registration, the algorithm calculates statistical dependency between images using entrophy-based measurements, often implemented through mi() custom functions or MATLAB's imregister() with 'mutualinfo' parameter. The edge-interactive variance method employs edge detection operators like Sobel (edge(I,'sobel')) or Canny, followed by variance calculation along detected edges using var() function across translational/rotational transformations. These criteria enable superior image matching precision, with mutual information excelling in multimodal registration where pixel intensity relationships differ, while edge-based variance performs optimally when structural boundaries provide primary alignment cues. This image registration methodology proves particularly valuable in medical imaging processing and computer vision applications, where techniques like imwarp() with optimized transform matrices facilitate accurate spatial alignment. Through proper image registration, we enhance information extraction and analysis capabilities, creating robust foundations for subsequent research and computational processing pipelines.