A constrained total variation model for single image dehazing
作者：
Highlights：
• highlights
• Propose a new formulation to describe a hazy image by combining the Koschmieder’s law and the Retinex theory.
• Propose a variational model to convert the problem of estimating the depth of scene to a constrained minimization problem.
• Prove the existence and uniqueness of solution of the proposed model;
• Develop an algorithm for numerical solution of our model by combining alternating minimization with fast gradient projection.
• Experiments show that our model has the best visual effect and the highest average PSNR compared to six relevant models in the literature.
摘要
highlights•Propose a new formulation to describe a hazy image by combining the Koschmieder’s law and the Retinex theory.•Propose a variational model to convert the problem of estimating the depth of scene to a constrained minimization problem.•Prove the existence and uniqueness of solution of the proposed model;•Develop an algorithm for numerical solution of our model by combining alternating minimization with fast gradient projection.•Experiments show that our model has the best visual effect and the highest average PSNR compared to six relevant models in the literature.
论文关键词：Dehazing,Total variation,Variational method,Gradient projection algorithm
论文评审过程：Received 20 October 2017, Revised 3 February 2018, Accepted 4 March 2018, Available online 20 March 2018, Version of Record 28 March 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.03.009
