Histogram Optimization-Based Image Dehazing Technology

MATLAB 197K 280 views 2 downloads 1 credits
Tags:

Resource Overview

This case study implements image dehazing experiments using global histogram equalization and local histogram equalization algorithms, with Retinex enhancement algorithm included as an extension to histogram-based dehazing approaches. The implementation features a GUI design with menu-driven algorithm selection, comparative histogram displays before and after processing, and detailed parameter analysis for performance evaluation.

Detailed Documentation

For image dehazing experiments, this case study employs global histogram equalization and local histogram equalization algorithms to perform image dehazing, while incorporating the Retinex enhancement algorithm as an extension to histogram-based dehazing methods. The implementation utilizes GUI design software with menu-driven integration of different dehazing algorithms, enabling comparative analysis of dehazing effects through side-by-side histogram displays before and after processing. During experimentation, we provide detailed descriptions and analysis of parameter adjustments to facilitate better understanding and evaluation of each algorithm's effectiveness and performance characteristics. Key implementation aspects include histogram equalization functions for contrast enhancement and Retinex algorithms for illumination correction, with parameter tuning interfaces for optimal results.

You May Also Like