Depth and Reflection Total Variation for Single Image Dehazing

TL;DR

This paper introduces a novel single image dehazing method that combines depth and reflection modeling with total variation regularization, effectively restoring clear and vivid images from hazy inputs.

Contribution

It proposes a new model integrating depth and reflection assumptions with total variation regularization, solved via an efficient optimization scheme, with theoretical analysis included.

Findings

Effective haze removal demonstrated on numerical examples

Restores vivid and contrastive hazy images

Outperforms some existing dehazing methods

Abstract

Haze removal has been a very challenging problem due to its ill-posedness, which is more ill-posed if the input data is only a single hazy image. In this paper, we present a new approach for removing haze from a single input image. The proposed method combines the model widely used to describe the formation of a haze image with the assumption in Retinex that an image is the product of the illumination and the reflection. We assume that the depth and reflection functions are spatially piecewise smooth in the model, where the total variation is used for the regularization. The proposed model is defined as a constrained optimization problem, which is solved by an alternating minimization scheme and the fast gradient projection algorithm. Some theoretic analyses are given for the proposed model and algorithm. Finally, numerical examples are presented to demonstrate that our method can restore vivid and contrastive hazy images effectively.