Reconstruction Loss Minimized FCN for Single Image Dehazing

TL;DR

This paper introduces a novel Fully Convolutional Neural Network that jointly estimates environmental illumination and scene transmittance to effectively dehaze images, especially under non-uniform lighting conditions.

Contribution

It proposes a new CNN model trained with a custom loss function based on image reconstruction, incorporating a multilevel approach for better scale invariance in dehazing.

Findings

Performs well against state-of-the-art methods

Effective under diverse lighting conditions

Handles non-uniform illumination scenarios

Abstract

Haze and fog reduce the visibility of outdoor scenes as a veil like semi-transparent layer appears over the objects. As a result, images captured under such conditions lack contrast. Image dehazing methods try to alleviate this problem by recovering a clear version of the image. In this paper, we propose a Fully Convolutional Neural Network based model to recover the clear scene radiance by estimating the environmental illumination and the scene transmittance jointly from a hazy image. The method uses a relaxed haze imaging model to allow for the situations with non-uniform illumination. We have trained the network by minimizing a custom-defined loss that measures the error of reconstructing the hazy image in three different ways. Additionally, we use a multilevel approach to determine the scene transmittance and the environmental illumination in order to reduce the dependence of the estimate on image scale. Evaluations show that our model performs well compared to the existing state-of-the-art methods. It also verifies the potential of our model in diverse situations and various lighting conditions.