Image Denoising Using MATLAB This repository contains the code, report, and data for the Image Denoising project developed using MATLAB. The project explores and implements various image denoising filters to enhance image quality by reducing different types of noise (e.g., Gaussian, salt-and-pepper, speckle).

Features Implementation of multiple filters: Adaptive Riesz Mean Filter Alpha Trimmed Mean Filter Geometric Mean Filter Median Filters (Basic and Modified) Sector Rotation Filter Recursive Spline Interpolation Filter Performance evaluation of filters using metrics like Peak Signal-to-Noise Ratio (PSNR). Comparison of filter effectiveness for various noise types and intensities. Project Structure CODE_COMPILATION.docx: Detailed MATLAB code for all implemented filters. final report.docx: Comprehensive report explaining algorithms, flowcharts, and results. Excel_filters_final.xlsx: Performance data and PSNR values for all filters across different noise types. Getting Started Prerequisites MATLAB installed on your system. Basic understanding of image processing concepts. Running the Code Clone this repository: bash Copy code git clone https://github.com/yourusername/image-denoising-matlab.git Open MATLAB and navigate to the repository folder. Load your noisy image into the workspace. Run the filter scripts provided in the code file. Example Here's a sample usage for the Basic Median Filter:

matlab Copy code % Load noisy image noisy_image = imread('noisy_image.png');

% Apply Basic Median Filter denoised_image = basic_median_filter(noisy_image);

% Display results imshow(denoised_image); Results The filters were tested on various types of noise, including:

Salt-and-Pepper Noise Gaussian Noise Speckle Noise Key Finding: Adaptive Riesz Mean Filter and Alpha Trimmed Mean Filter consistently provided the best results for high-density noise removal.

Contributors Chaitanya License This project is licensed under the MIT License. See the LICENSE file for details.