Histogram Plotting, Grayscale Stretching, and Image Equalization with MATLAB Implementation

This text presents image processing codes developed using MATLAB, encompassing functionalities for histogram plotting, grayscale stretching, image equalization, and frequency-domain smoothing filtration. The implementation employs MATLAB's built-in functions like imhist() for histogram visualization with automatic bin calculation, while grayscale stretching utilizes linear contrast enhancement through pixel value remapping. Image equalization is implemented via histogram equalization algorithms that redistribute intensity values using cumulative distribution functions. Frequency-domain filtering incorporates Fourier transform techniques with low-pass filters for noise reduction. These codes demonstrate compact, practical implementations with well-documented comments explaining each processing stage. Furthermore, the discussion can be expanded to address the significance of image processing and its contemporary technological applications. Image processing constitutes a critical technology enabling image quality enhancement, meaningful information extraction, and diverse visual effects realization. In today's digital ecosystem, image processing finds extensive applications across medical imaging, security surveillance, artificial intelligence, and numerous other domains. Consequently, comprehending and mastering image processing techniques remains essential for professionals engaged in related fields.