Night Time Haze and Glow Removal using Deep Dilated Convolutional Network

TL;DR

This paper presents a deep learning architecture that effectively removes glow and haze from nighttime images, improving image clarity and quality in challenging lighting conditions.

Contribution

Introduces a novel DeGlow-DeHaze iterative deep learning model specifically designed for nighttime scenes with glow and haze effects.

Findings

Outperforms state-of-the-art methods in image quality

Faster computation speed

Effective on real nighttime images

Abstract

In this paper, we address the single image haze removal problem in a nighttime scene. The night haze removal is a severely ill-posed problem especially due to the presence of various visible light sources with varying colors and non-uniform illumination. These light sources are of different shapes and introduce noticeable glow in night scenes. To address these effects we introduce a deep learning based DeGlow-DeHaze iterative architecture which accounts for varying color illumination and glows. First, our convolution neural network (CNN) based DeGlow model is able to remove the glow effect significantly and on top of it a separate DeHaze network is included to remove the haze effect. For our recurrent network training, the hazy images and the corresponding transmission maps are synthesized from the NYU depth datasets and consequently restored a high-quality haze-free image. The experimental results demonstrate that our hybrid CNN model outperforms other state-of-the-art methods in terms of computation speed and image quality. We also show the effectiveness of our model on a number of real images and compare our results with the existing night haze heuristic models.