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

SLLEN: Semantic-aware Low-light Image Enhancement Network

How to effectively explore semantic feature is vital for low-light image enhancement (LLE). Existing methods usually utilize the semantic feature that is only drawn from the semantic map produced by high-level semantic segmentation network (SSN). However, if the semantic map is not accurately estimated, it would affect the high-level semantic feature (HSF) extraction, which accordingly interferes with LLE. In this paper, we develop a simple yet effective two-branch semantic-aware LLE network (SLLEN) that neatly integrates the random intermediate embedding feature (IEF) (i.e., the information extracted from the intermediate layer of semantic segmentation network) together with the HSF into a unified framework for better LLE. Specifically, for one branch, we utilize an attention mechanism to integrate HSF into low-level feature. For the other branch, we extract IEF to guide the adjustment of low-level feature using nonlinear transformation manner. Finally, semantic-aware features obtained from two branches are fused and decoded for image enhancement. It is worth mentioning that IEF has some randomness compared to HSF despite their similarity on semantic characteristics, thus its introduction can allow network to learn more possibilities by leveraging the latent relationships between the low-level feature and semantic feature, just like the famous saying "God rolls the dice" in Physics Nobel Prize 2022. Comparisons between the proposed SLLEN and other state-of-the-art techniques demonstrate the superiority of SLLEN with respect to LLE quality over all the comparable alternatives

Visibility Restoration for Single Hazy Image Using Dual Prior Knowledge

Single image haze removal has been a challenging task due to its super ill-posed nature. In this paper, we propose a novel single image algorithm that improves the detail and color of such degraded images. More concretely, we redefine a more reliable atmospheric scattering model (ASM) based on our previous work and the atmospheric point spread function (APSF). Further, by taking the haze density spatial feature into consideration, we design a scene-wise APSF kernel prediction mechanism to eliminate the multiple-scattering effect. With the redefined ASM and designed APSF, combined with the existing prior knowledge, the complex dehazing problem can be subtly converted into one-dimensional searching problem, which allows us to directly obtain the scene transmission and thereby recover visually realistic results via the proposed ASM. Experimental results verify that our algorithm outperforms several state-of-the-art dehazing techniques in terms of robustness, effectiveness, and efficiency

A Single Image Dehazing Method Using Average Saturation Prior

Outdoor images captured in bad weather are prone to yield poor visibility, which is a fatal problem for most computer vision applications. The majority of existing dehazing methods rely on an atmospheric scattering model and therefore share a common limitation; that is, the model is only valid when the atmosphere is homogeneous. In this paper, we propose an improved atmospheric scattering model to overcome this inherent limitation. By adopting the proposed model, a corresponding dehazing method is also presented. In this method, we first create a haze density distribution map of a hazy image, which enables us to segment the hazy image into scenes according to the haze density similarity. Then, in order to improve the atmospheric light estimation accuracy, we define an effective weight assignment function to locate a candidate scene based on the scene segmentation results and therefore avoid most potential errors. Next, we propose a simple but powerful prior named the average saturation prior (ASP), which is a statistic of extensive high-definition outdoor images. Using this prior combined with the improved atmospheric scattering model, we can directly estimate the scene atmospheric scattering coefficient and restore the scene albedo. The experimental results verify that our model is physically valid, and the proposed method outperforms several state-of-the-art single image dehazing methods in terms of both robustness and effectiveness

An Effective and Robust Single Image Dehazing Method Using the Dark Channel Prior

In this paper, we propose a single image dehazing method aiming at addressing the inherent limitations of the extensively employed dark channel prior (DCP). More concretely, we introduce the Gaussian mixture model (GMM) to segment the input hazy image into scenes based on the haze density feature map. With the segmentation results, combined with the proposed sky region detection method, we can effectively recognize the sky region where the DCP cannot well handle this. On the basis of sky region detection, we then present an improved global atmospheric light estimation method to increase the estimation accuracy of the atmospheric light. Further, we present a multi-scale fusion-based strategy to obtain the transmission map based on DCP, which can significantly reduce the blocking artifacts of the transmission map. To further rectify the error-prone transmission within the sky region, an adaptive sky region transmission correction method is also presented. Finally, due to the segmentation-blindness of GMM, we adopt the guided total variation (GTV) to tackle this problem while eliminating the extensive texture details contained in the transmission map. Experimental results verify the power of our method and show its superiority over several state-of-the-art methods