Improved Single Image Haze Removal for Intelligent Driving
- PDF / 1,642,142 Bytes
- 7 Pages / 612 x 792 pts (letter) Page_size
- 0 Downloads / 200 Views
Improved Single Image Haze Removal for Intelligent Driving Yi Laia,b,c,*, Q. Wanga,b,c,**, and R. Chena,*** a
School of Telecommunication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an, 710121 China b Key Laboratory of Electronic Information Application Technology for Scene Investigation, Ministry of Public Security, Xi’an, 710121 China c Key Laboratory of Spectral Imaging Technology of Chinese Academy of Sciences, Xi’an, 710119 China * e-mail: [email protected] ** e-mail: [email protected] *** e-mail: [email protected]
Abstract—Haze often degrades the contrast and limits the visibility of scenes, and as a result it has a negative impact on the safe driving of intelligent vehicles. In order to solve this problem and enhance the quality of hazy images, this paper proposes an improved single image haze removal method. The main works include two parts. On the one hand, an improved atmospheric light estimation method is addressed to achieve an accurate estimation of the atmospheric light. On the other hand, the transmission map is refined with a composite filter using bilateral one followed by adaptive parameter adjustment on the transmission function. The experimental results show that the presented approach can obtain substantial improvements on the color and the detail recovery on both synthetic and real-world datasets. Keywords: haze removal, boundary constraint, atmospheric light estimation, bilateral filter DOI: 10.1134/S1054661820030177
1. INTRODUCTION With the rapid development of sensors and artificial intelligence technology, it is the key period for intelligent driving technology from conceptualization to practical application. It is an important premise and foundation for future wide application of this technology to drive safely and orderly in harsh environment. Fog and haze scene is one of the common bad environments. Outdoor images captured under this foggy weather condition are often seriously degraded by the turbid medium (e.g., dust, water-droplets) in the atmosphere. And the distant objects in the degraded images lose the color fidelity and become blurred with their surrounding, as demonstrated in Fig. 1a. Thus, effective and robust haze removal (or dehazing) methods are strongly desired in intelligent driving, computational photography, and computer vision applications, etc. [1, 2]. Early haze removal approaches [3–6] often require multiple images or additional information of the same scene. Although these methods can ameliorate the visibility of hazy images, they cannot be used in cases where additional information or multiple observations cannot be recorded. Therefore, single image haze removal has been a hot spot of research given its wider application range [7]. Recently, a lot of significant
Received December 16, 2019; revised February 26, 2020; accepted March 2, 2020
progresses have been made since the introduction of single image haze removal. These image dehazing algorithms [1, 2, 8–14] remove the haze under certain priors or assumptions, such as
Data Loading...
