MATLAB Implementation for Digital Image Processing with Noise Addition

Digital image processing in MATLAB encompasses various operations and enhancements for images, including noise addition and processing techniques. This technical field involves multiple algorithms and methodologies to simulate and remove different types of noise from images, thereby improving image quality and clarity. Key implementation aspects include using MATLAB's Image Processing Toolbox functions such as: - imnoise() function for adding various noise types (Gaussian, salt & pepper, speckle) - Custom algorithms for noise simulation and filtration - Parameter optimization for achieving optimal processing results These processing techniques find applications across multiple domains including medical imaging analysis, security surveillance systems, and image recognition technologies. MATLAB's robust image processing capabilities allow developers to implement sophisticated algorithms through functions like filter2() for spatial filtering, medfilt2() for median filtering, and wavelet-based denoising approaches. The platform enables parameter adjustment and algorithm customization to achieve optimal balance between noise removal and image detail preservation.