Histogram Optimization-Based Image Dehazing Technique

This paper discusses the implementation of a histogram optimization-based image dehazing technique using MATLAB. The technique enhances image clarity and visibility by optimizing the histogram distribution of fog-affected images. The implementation process involves fog removal operations combined with histogram optimization algorithms to improve visual quality. Key implementation aspects include: - Preprocessing steps for fog density estimation using atmospheric scattering model - Histogram equalization and specification techniques for contrast enhancement - Adaptive histogram manipulation to restore natural color distribution - MATLAB functions utilized: histeq for histogram equalization, imadjust for contrast adjustment, and custom algorithms for fog density mapping This technology holds significant value in computer vision and image processing applications, particularly for enhancing blurred and hazy images to achieve clearer, more observable results. The method effectively addresses visibility degradation caused by atmospheric conditions through systematic histogram manipulation and pixel intensity redistribution.