Smart and real-time image dehazing on mobile devices

Abstract

Haze is one of the common factors that degrades the visual quality of the images and videos. This diminishes contrast and reduces visual efficiency. The ALS (Atmospheric light scattering) model which has two unknowns to be estimated from the scene: atmospheric light and transmission map, is commonly used for dehazing. The process of modelling the atmospheric light scattering is complex and estimation of scattering is time consuming. This condition makes dehazing in real-time difficult. In this work, a new approach is employed for dehazing in real-time which reads the orientation sensor of mobile device and compares the amount of rotation with a pre-specified threshold. The system decides whether to recalculate the atmospheric light or not. When the amount of rotation is little means there are only subtle changes to the scene, it uses the pre-estimated atmospheric light. Therefore, the system does not need to recalculate it at each time instant and this approach accelerates the overall dehazing process. 0.07s fps (frame per second) per frame processing time (~15fps) is handled for 360p imagery. Frame processing time results show that our approach is superior to the state-of-the-art real-time dehazing implementations on mobile operating systems.

An end-to-end deep learning approach for real-time single image dehazing

“…Therefore, many studies [17,[19][20][21][22] have been conducted to decrease the computational complexity of the DCP algorithm. Moreover, an empirical study [27] to compare the performance of the efficient dehazing methods has been conducted.…”

“…A recent study [20] proposed a method for estimating atmospheric light based on a weighting scheme and substituted morphological filtering for the soft matting method to reduce the computation time of the DCP algorithm. Cimtay [22] proposed a method to increase the processing speed of the dehazing method by reusing previously calculated atmospheric light information after analyzing changes in the orientation of mobile devices. Although these methods decrease the processing time of the DCP algorithm, DCP has limited dehazing performance with regard to PSNR and SSIM.…”

“…A few studies [17,[19][20][21][22] have been conducted to decrease the latency of dehazing methods. Methods to increase the processing speed of a dehazing algorithm were proposed by replacing complex operations in the DCP algorithm with low-complexity operations.…”

“…Methods to increase the processing speed of a dehazing algorithm were proposed by replacing complex operations in the DCP algorithm with low-complexity operations. In addition, methods that utilize device properties, such as digital signal processors (DSPs) [19] or mobile devices [22], have been proposed to reduce the latency of dehazing algorithms. However, these methods mainly focus on the DCP algorithm, which is a representative prior-knowledge-based method, and have limited dehazing performance with regard to PSNR and SSIM.…”

Band Selection for Dehazing Algorithms Applied to Hyperspectral Images in the Visible Range

“…The objective of these methods is to obtain an image as free as possible from either haze or fog. Image dehazing is an indispensable step in many applications such as air and maritime transport, surveillance, driver assistance systems, remote sensing, agronomy, archaeology, astronomy, medical sciences and environmental studies [6][7][8][9][10][11][12][13][14][15][16][17][18][19]. In the last decade, a large number of dehazing algorithms have been proposed, and this has become a growing area of research and development.…”