Image Tiling, Stitching, and Cropping Techniques
Several practical mini-programs for tiling images, stitching divided image blocks, and cropping images
Image Block Partitioning and Compressive Sensing Algorithm Reconstruction
Implementation of image block partitioning and reconstruction using compressive sensing algorithms with enhanced code-level optimization descriptions
Block-Based Image Processing Using LBP
Implementing Local Binary Patterns (LBP) for block-based image segmentation enables microscopic feature enhancement through localized texture analysis
MATLAB Implementation of Image Block Processing with Advanced Features
This program implements image segmentation techniques including block processing, convex hull operations, and image partitioning. Essential for computer vision and image processing enthusiasts, featuring detailed code explanations and algorithm implementations.
Performing Discrete Fourier Transform (DFT) on Image Sub-blocks
Dividing a 256x256 image into 8x8 sub-blocks, performing DFT transformation, and comparing image results from spatial domain versus frequency domain subsampling and interpolation operations
Image Block Processing
Image block processing divides images into smaller segments for enhanced analysis, feature extraction, and computer vision applications. Common implementations involve matrix partitioning, sliding window techniques, and GPU-accelerated block operations.
Image Block Processing with Mean and Variance Calculation for Each Block
Divide an image into blocks, compute the mean and variance for each block, and provide visualized results for easy observation and analysis. Includes code implementation details for image segmentation and statistical computation.
Video Segmentation Implementation Using MATLAB
This MATLAB implementation performs video segmentation by reading video frames, comparing similarity between adjacent frames, dividing images into blocks, and analyzing block-level similarities using difference calculation and histogram comparison techniques.
Processing with Contrast-Based Pixel Selection on Wavelet Decomposition Foundation
This method involves partitioning the image into blocks and performing wavelet decomposition to obtain low-frequency and high-frequency components. It calculates the contrast for each block to classify them into clear blocks and blurred blocks, with adjacent areas between clear and blurred blocks defined as boundary regions. During fusion, clear blocks are directly selected as the corresponding fused blocks. For boundary regions, a contrast-based pixel selection method is applied on the wavelet decomposition basis, implementing weighted fusion algorithms to optimize edge transitions and enhance image clarity.