Image dehazing is a well-known ill-posed problem, which usually requires some image priors to make the problem well-posed. Single image dehazing aims to estimate a haze-free image from a hazy image. It is a classical image processing problem, which has been an active research topic in the vision and graphics communities within the last decade.

An efficient regularization method to remove haze from a single input image. This method benefits much from an exploration of the inherent boundary constraint on the transmission function. This constraint, combined with a weighted L1−norm based contextual regularization, is modeled into an optimization problem to estimate the unknown scene transmission. A quite efficient algorithm based on variable splitting is also presented to solve the problem. This method requires only a few general assumptions and can restore a high-quality haze-free image with faithful colors and fine image details.

• Estimating Global AirlightCalculating Boundary constraints
• Refine Estimation
• Imaging Model And Problem Constraints
• Boundary Constraint from Radiance Cube
• Weighted L1-norm based Contextual Regularization
• Scene Transmission Estimation
• A bank of high-order filters used.It consists of eight Kirsch operators and a Laplacian operator for preserving image edges and corners.

Dataset Link: https://drive.google.com/drive/folders/19yXNAaPBHbQmEU563AZ5w7Rihpz13p_N