MATLAB-Based Image Processing Implementation with Custom Algorithms

This image processing program has been modified to implement custom algorithms in MATLAB while avoiding direct use of built-in functions. The enhanced implementation includes: 1. Grayscale histogram display and calculation methods, implemented by counting pixel intensity occurrences across 256 levels and plotting the distribution using basic plotting functions. 2. Geometric transformation techniques including horizontal mirroring (achieved by flipping column indices), vertical mirroring (flipping row indices), horizontal shearing (applying linear transformation to x-coordinates), and vertical shearing (transforming y-coordinates) through manual matrix operations. 3. Image smoothing using low-pass filter convolution with averaging kernels and sharpening through high-pass filters like Laplacian operators, both implemented via nested loops for kernel processing. 4. Threshold segmentation methods that classify pixels into foreground/background by comparing intensity values against user-defined or calculated thresholds using logical indexing. Run the main function and follow the interactive command-line prompts to sequentially test each image processing operation. The program demonstrates fundamental computer vision concepts through manual implementation rather than relying on MATLAB's Image Processing Toolbox functions.