Real-Time Haze Removal Using Normalised Pixel-Wise Dark-Channel Prior and Robust Atmospheric-Light Estimation

Yutaro Iwamoto,Yen-Wei Chen,Naoaki Hashimoto

doi:10.3390/app10031165

Abstract

This study proposes real-time haze removal from a single image using normalised pixel-wise dark-channel prior (DCP). DCP assumes that at least one RGB colour channel within most local patches in a haze-free image has a low-intensity value. Since the spatial resolution of the transmission map depends on the patch size and it loses the detailed structure with large patch sizes, original work refines the transmission map using an image-matting technique. However, it requires high computational cost and is not adequate for real-time application. To solve these problems, we use normalised pixel-wise haze estimation without losing the detailed structure of the transmission map. This study also proposes robust atmospheric-light estimation using a coarse-to-fine search strategy and down-sampled haze estimation for acceleration. Experiments with actual and simulated haze images showed that the proposed method achieves real-time results of visually and quantitatively acceptable quality compared with other conventional methods of haze removal.

Highlights

In recent years, self-driving vehicles, underwater robots, and remote sensing have attracted attention; such applications employ fast and robust image-recognition techniques
We used haze and haze-free images downloaded from the Flickr website [21] and MATLAB
We propose a haze-removal method using a normalised pixel-wise dark-channel prior (DCP) method

Summary

Introduction

Self-driving vehicles, underwater robots, and remote sensing have attracted attention; such applications employ fast and robust image-recognition techniques. Images of outdoor or underwater scenes have poor image quality because of haze (Figure 1a), affecting image recognition. To solve this problem, many haze removal techniques were proposed, and these techniques can be classified into non-learning-based and learning-based approaches. Learning-based approaches employ random forest [8], colour-attenuation and prior-based brightness-saturation relation [9] and deep learning [10,11]. These methods can achieve accurate and fast haze removal compared with conventional non-learning-based approaches

Methods

Results

Conclusion