Removal of Haze Effects from Images with MATLAB Implementation

Resource Overview

Implementation of haze removal from images using MATLAB with four distinct approaches: dark channel prior, color restoration, global contrast enhancement, and degradation model-based methods.

Detailed Documentation

We can implement haze removal from images using MATLAB. Four different approaches are available for attempting haze removal: Method 1: Dark Channel Prior Haze Removal. This technique leverages the dark channel prior observed in natural images. The algorithm identifies the darkest regions in the image to estimate haze density and transmission rate, which is then applied globally to eliminate haze effects. In MATLAB implementation, this typically involves calculating minimum channel values using min() function across color channels, followed by guided filtering for transmission refinement. Method 2: Color Restoration-Based Haze Removal. This approach mitigates haze impacts by recovering color information through color compensation techniques. The method employs color correction algorithms to neutralize haze-induced color shifts, resulting in clearer and more realistic images. MATLAB implementation often uses color balance adjustments and saturation enhancement functions like imadjust() or histeq(). Method 3: Global Contrast Enhancement Haze Removal. By enhancing overall image contrast, this method reduces haze interference with image details. It improves visibility of fine details through contrast stretching algorithms. MATLAB implementation typically involves contrast-limited adaptive histogram equalization (adapthisteq) or global histogram equalization operations. Method 4: Degradation Model-Based Haze Removal. This technique reconstructs haze-affected details by establishing an image degradation model. The algorithm models haze-induced blur and contrast reduction, then applies deconvolution and enhancement algorithms to restore image details. MATLAB implementation may utilize deconvwnr() for Wiener filtering or other iterative restoration algorithms with point spread function estimation. These methods provide comprehensive solutions for image dehazing. Should you have any technical questions regarding implementation details or algorithm selection, please feel free to inquire.