Image Fusion Based on Discrete Wavelet Transform with MATLAB Implementation

Resource Overview

A comprehensive MATLAB program for discrete wavelet transform-based image fusion capable of processing grayscale images in any format, featuring multi-level decomposition and customizable fusion rules

Detailed Documentation

This document presents a detailed MATLAB implementation of image fusion based on discrete wavelet transform (DWT). The program architecture includes core functions for wavelet decomposition using filters like 'db4' or 'sym8', with customizable decomposition levels typically ranging from 2 to 5 layers. The implementation handles various grayscale image formats (JPG, PNG, BMP, TIFF) through MATLAB's imread function with automatic format detection. Beyond the fundamental fusion algorithm that applies different fusion rules (such as maximum selection or weighted average) to approximation and detail coefficients, the program incorporates additional functionalities including contrast enhancement techniques like histogram equalization and spatial filtering operations (Gaussian, median filters). The code structure features modular design with separate functions for wavelet decomposition, coefficient fusion, and image reconstruction, allowing users to modify parameters through a configuration file or GUI interface. Key adjustable parameters include wavelet type selection, decomposition level, fusion rule thresholds, and enhancement parameters, all accessible through well-documented configuration options. The package includes a comprehensive user manual detailing function calls, parameter explanations, and example workflows. We believe this implementation serves as a valuable tool in image processing applications, providing high-quality, efficient fusion solutions with clear computational efficiency advantages for multi-source image integration. The algorithm workflow follows these steps: 1) Input image preprocessing and format conversion, 2) Multi-level DWT decomposition using wavedec2 function, 3) Application of fusion rules to approximation and detail coefficients, 4) Inverse DWT reconstruction using waverec2, 5) Optional post-processing with enhancement filters. This structured approach ensures robust performance across various image types and fusion scenarios.