Imaging-Model-Based Visibility Recovery for Single Hazy Images

University of Technology Sydney. Faculty of Engineering and Information Technology.Low-quality images captured in hazy weather can seriously impair the proper functioning of vision system. Although many meaningful works have been done to realize the haze removal, there are still two key issues remain unsolved. The first one is the long processing time attributed to the involved tools; the second one is existing prior employed in state-of-the-art approaches cannot be suitable for all situations. To address such problems, a series of haze removal techniques have been developed. The main contributions of this dissertation can be summarized as the following. For efficiency, a gamma correction prior is proposed, which can be used to synthesize a homogeneous virtual transformation for an input. Relying this prior and atmospheric scattering model (ASM), a fast image dehazing method called IDGCP is developed, which converts single image haze removal into multiple images haze removal task. Unlike the IDGCP, another solution for accelerating dehazing (VROHI) is to utilize a low complexity model, i.e., the additive haze model (AHM), to simulate the hazy image. AHM is used on remote sensing data restoration, thus the first step of VROHI is to modify the AHM to make it suitable for outdoor images. The modified AHM enables to achieve single image dehazing by finding two constants related to haze thickness. To overcome the uneven illumination issue, the atmospheric light in ASM is replaced or redefined as a scene incident light, leading to a scene-based ASM (Sb-ASM). Based on this Sb-ASM, an effective image dehazing technique named IDSL is proposed by using a supervised learning strategy. In IDSL, the transmission estimation is simplified to simple calculation on three components by constructing a lineal model for estimating the transmission. According to previous Sb-ASM and the fact that inhomogeneous atmosphere phenomenon does exist in real world, a pixel-based ASM (Pb-ASM) is redefined to handle the inhomogeneous haze issue. Benefitting from this Pb-ASM, a single image dehazing algorithm called BDPK that uses Bayesian theory is developed. In BDPK, single image dehazing problem is transformed into a maximum a-posteriori probability one. To achieve high efficiency and high quality dehazing for remote sensing (RS) data, an exponent-form ASM (Ef-ASM) is proposed by using equivalence infinitesimal theorem. By imposing the bright channel prior and dark channel prior on Ef-ASM, scene albedo restoration formula (SARF) used for RGB-channel RS image is deduced. Based on Rayleighąŕs law, SARF can be expanded to achieve haze removal for multi-spectral RS data

曇り度の自己調整によるヘイズ除去に関する研究

With the increase in industrial production and human activities, the concentration of atmospheric particulate matter is increasing substantially, leading to more frequent occurrence of fog and haze. Limited visibility caused by suspended particles in the air, such as fog and haze, is a major problem for many computer vision applications. The scenes captured by such computer vision systems suffer from poor visibility, low contrast, dimmed brightness, low luminance, and distorted color, which makes detection of objects within a scene more difficult. Therefore, visibility improvement, contrast, and feature enhancement of images and videos captured in inclement weather, which is referred to as dehazing, is inevitable. Haze removal is a difficult problem due to the inherent ambiguity between haze and the underlying scene. Model-based single-image dehazing methods are physically sound and produce qualitatively good results; however, more attention to dehazing quality rather than applicability leads to real-time applications that may not always be sufficiently fast. And in addition, the certain limitations exist, such as color bias and an inability of dealing with the sky area. The objectives of this study are to estimate the degree of haze from a single image automatically, and label the image with a haze factor, to propose a self-adjustment dehazing method using the degree of haze and evaluate the performance of the proposed dehazing method. In this research, based on atmospheric scattering model analysis and statistics of various outdoor images, the following conclusions can be drawn. Clear-day images have higher contrast than images plagued by bad weather. In addition, in most local regions, including the sky, hazed images have larger minimum values for most color channel pixels. Relying on these two observations, an estimate function that is related to a haze removal constant parameter has been developed to label a foggy image with different degrees of haze. By modifying the dark channel in advance, the proposed method reduces computational cost while providing promising dehazed results for real-time applications. The transmission estimation is performed by obtaining the minimum value only from a relevant pixel, or the mean filter of the minimum values of its neighboring pixels. The main advantage of the proposed algorithm compared to others is its processing speed. Its complexity is a linear function of the number of image pixels. Another advantage is similar image processing quality using the Dark Channel Prior method. Furthermore, labeling images with different haze-degree can make batch processing of an image set with hazy and haze-free images possible. Finally, the degree of haze removal can be adjusted adaptively with a built-in function that makes the dehazed images look more natural. The experimental results clearly indicate that the proposed approach achieves good performance for enhanced visibility, processing speed, and stability, which makes the proposed method applicable for real-time requirements.室蘭工業大学 (Muroran Institute of Technology)博士（工学