Open AccessDark channel prior based video dehazing algorithm with sky preservation and its embedded system realization for ADAS applications
Author(s) -
Chia-Chi Tsai,
ChingYih Lin,
Jiun-In Guo
Publication year - 2019
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.27.011877
Subject(s) - computer science , filter (signal processing) , computer vision , channel (broadcasting) , algorithm , flicker , artificial intelligence , distortion (music) , noise (video) , median filter , computational complexity theory , sky , real time computing , image processing , computer graphics (images) , image (mathematics) , telecommunications , amplifier , physics , bandwidth (computing) , astrophysics
Dark Channel Prior (DCP) is one of the significant dehazing methods based upon the observation of the key features of the haze-free images. But it has disadvantages; high computational complexity, over-enhancement in the sky region, flickering artefacts in video processing, and poor dehazing. Therefore, we propose improved solutions to solve the aforementioned drawbacks. First, we adopt the fast one-dimensional filter, look-up table, and program optimization to reduce the computational complexity. Next, we follow by using a part of the guided filter for sky detection and to preserve the sky region from noise by avoiding over recovery. Then, we propose an airlight update strategy and adjust the radius of a guided filter to reduce the flickering artifacts and also propose an airlight estimation method to produce the better dehazing result as the final step of our algorithm. The improved results from our proposed algorithm are stable and are obtained from the real-time processing suitable for ADAS, surveillance, and monitoring systems. The implementation of the proposed algorithm has yielded a processing speed of 75 fps and 23 fps respectively on an NVIDIA Jetson TX1 embedded platform and Renesas R-Car M2, both on D1 (720x480) resolution videos.