Histogram Optimization-Based Image Dehazing Technology

In recent research on image dehazing, algorithms based on histogram optimization have demonstrated significant performance advantages. The core concept of this algorithm involves preprocessing images using histogram analysis before feeding them into an optimization model for processing. Unlike traditional image dehazing methods, this approach clusters pixels with similar values together to better restore scene depth information. From an implementation perspective, this typically involves calculating the histogram distribution of the input hazy image, applying histogram equalization or specification techniques to enhance contrast, and then using optimization algorithms to refine the atmospheric scattering model parameters. Additionally, the algorithm can adapt to different image scenarios by adjusting parameters such as cluster size, optimization thresholds, and regularization weights, thereby improving dehazing effectiveness. Key functions in the implementation often include histogram calculation routines, clustering algorithms like k-means for pixel grouping, and optimization solvers for minimizing the dehazing cost function. The adaptability makes histogram optimization-based dehazing technology one of the current research hotspots in the image processing field, particularly suitable for handling varying atmospheric conditions and scene complexities.