Currently available dehazing methods, whether based on hand-crafted priors or learned from datasets, typically ignore the brightness consistency between hazy images and their dehazed results, which often leads to over-enhancement and color cast. To address this issue, we first investigate a patch-wise nonlinear brightness prior (PNBP) that explicitly characterizes the relationship between the brightness of hazy patches and that of their clear counterparts. By combining PNBP with the atmospheric scattering model, the single image dehazing problem can be recast as a restoration formula with only three parameters, substantially shrinking the solution space for haze removal. Under a multi-objective joint optimization that simultaneously considers information gain, exposure, and preservation of the pixel-histogram distribution, this restoration formula can directly produce high-quality dehazed images. Thanks to PNBP, our method inherits brightness consistency from the prior and thereby avoids the risk of over-enhancement while reducing the possibility of color cast. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art approaches in terms of defogging quality, robustness, and computational efficiency.

Despite advances in single image dehazing, robust dehazing for real-world road traffic scenes remains challenging due to scarce paired data, traffic-specific geometry, and real-time constraints. To address this issue, we propose a novel image prior for road traffic scenes, termed scene geometry prior (SGP), which leverages depth cues derived from vanishing point (VP) to provide geometry-aware guidance and reduce reliance on paired training data. Our SGP comprises two components: a global SGP (G-SGP) that captures the global geometric distribution and a non-local SGP (NL-SGP) that corrects the errors, among obstructions belonging to the same category, in captured global distribution. Building on the proposed prior, we develop a lightweight and unsupervised road traffic image dehazing network (RTDnet). It consists of a main sub-network guided by the G-SGP to reconstruct the haze-free image, alongside two auxiliary sub-networks that leverage the NL-SGP and VP information to respectively estimate transmission map, and atmospheric light. During training, we introduce an atmospheric scattering model (ASM)-driven mutual-boost learning mechanism (ASM-ML), which is rooted in Bayesian theory and effectively integrates the strengths of different priors without mutual interference while distilling ASM-based physical knowledge into each sub-network. By coupling SGP with ASM-ML, RTDnet can be trained without paired traffic data by exploiting traffic-specific geometry, whose accurate guidance reduces the reliance on large model capacity and enables lightweight real-time deployment. Experiments demonstrate that our RTDnet surpasses state-of-the-art competitors in terms of restoration quality, efficiency, and model size. Moreover, its robust dehazing performance benefits downstream tasks operating in hazy conditions.

How to effectively explore semantic feature, especially the traffic semantics, is vital for Low-light image enhancement (LLE) in intelligent transportation systems. Existing methods usually utilize the semantic feature that is only drawn from the output produced by high-level semantic segmentation (SS) network. However, if the output is not accurately estimated, it would affect the high-level semantic feature (HSF) extraction, which accordingly interferes with LLE. To this end, we develop a simple and effective semantic-aware LLE network (SLLEN) composed of a LLE main-network (LLEmN) and a SS auxiliary-network (SSaN). In SLLEN, LLEmN integrates the random intermediate embedding feature (IEF), i.e., the information extracted from the intermediate layer of SSaN, together with the HSF into a unified framework for better LLE. SSaN is designed to act as a SS role to provide HSF and IEF. Moreover, thanks to a shared encoder between LLEmN and SSaN, we further propose an alternating training mechanism to facilitate the collaboration between them. Unlike currently available approaches, the proposed SLLEN is able to fully lever the semantic information, e.g., IEF, HSF, and SS dataset, to assist LLE, thereby leading to a more promising enhancement performance. Additionally, the proposed SLLEN can be applied into intelligent transportation system (ITS). The images enhanced by SLLEN are not only visually clear, but also can be better recognized by subsequent traffic semantics. 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.

In this paper, a novel image enhancement method, called the all-inclusive image enhancement (AIIE), is proposed that can effectively enhance the degraded images for improving the visibility of image content. These imageries were acquired under various types of weather conditions such as haze, low-light, underwater, and sandstorm, etc. One commonality shared by this class of noise is that the resulted degradations on visual quality or visibility are caused by low-frequency interference. Existing image enhancement methods lack the ability to deal with all types of degradations from this class, while our proposed AIIE offers a unified treatment for them. To achieve this goal, a statistical property is obtained from the study of the discrete cosine transform (DCT) of 1,000 high- and 1000 low-quality images on their DCT domains. It shows that the normalized DCT coefficients (between 0 and 1) of high-quality images has about 95% fall in the interval [0, 0.2]; for low-quality images, almost all the coefficients are in the same interval. This fundamental property, called the DCT prior (DCT-P), is instrumental to the development of our AIIE algorithm proposed in this paper. Since the proposed DCT-P delineates the attributes of high- and low-quality images clearly, it becomes a highly effective ‘tool’ to convert low-quality images to its enhanced version. Extensive experimental results have clearly validated the superior performance of the AIIE conducted on different types of deteriorated images in terms of visual quality and efficiency as well as significant advantages on computational complexity, which is essential for real-time applications.

Multi-scale fusion strategies have proven their efficient and effective performance in image dehazing tasks. However, inadequate feature fusion can lead to underutilizing local and global features. To this end, we propose a Fully Guided Network (FGN) for image dehazing. Specifically, we design a novel multi-scale aggregate attention (MAA), which aims to fully utilize early multi-scale features to guide the subsequent learning of the network. To prevent information redundancy, we develop an efficient multi-scale gated fusion module (MGFM) to control the information flow of different feature maps in the decoder stage. Based on MAA and MGFM, CNN-Transformer dual-branch block (CTDB) is constructed as the basic unit to achieve more refined image reconstruction. Extensive experiments on synthetic and real-world datasets demonstrate that FGN surpasses other state-of-the-art dehazing methods in terms of quantitative scores and recovery quality.

Infrared and visible data fusion (IVF) aims to generate a fused output that simultaneously highlights salient thermal radiation features and preserves texture information, which can not only grasp the necessary information for traffic movement, but also highlight the invisible objects that need to be dodged in intelligent transportation system (ITS). Therefore, IVF is capable of improving the environmental perception ability for various challenging traffic situations, e.g., foggy scenarios, rainy environments, and low-light illumination. However, current available IVF algorithms cannot offer a theoretical manner to integrate a priori knowledge and the network structure into a unified model. Moreover, they always fail to handle infrared and visible data pairs with different resolutions, which is a common occurrence in real ITS scenarios. To this end, this study develops a novel model-inspired unsupervised network termed IVF-Net. Specifically, an enhanced IVF model (IVFM), which pays more attention on detailed texture information and salient objects, is first established. According to proximal gradient theory, then we map this model into a deep network with learnable feature extraction parameters, aiming to draw on the strengths of the fusion model and deep learning to better describe the IVF task. Finally, a multiple task-driven loss function is designed to train the mapped network. Unlike previous work, our IVF-Net is motivated by IVFM, each layer in which has a semantic interpretability and a clear mission, thereby leading to a significantly enhanced fusion effect. Another advantage is that it is only composed of simple convolution-based structures, which ensures its lightweight and efficiency. Experiments demonstrate that IVF-Net can have a stronger ability to capture the key traffic information and highlight the salient feature of imperceptible objects, which makes it an excellent candidate to improve the reliability of subsequent applications in ITS.

In this letter, a robust and promising atmospheric scattering model (ASM)-based image dehazing technique called IDBP is developed, which overcomes the intrinsic limitation of available techniques based on single priors. It consists of two modules, i.e., an atmospheric light estimation (ALE) module and a multiple prior constraint (MPC) module. The ALE module is based on a new global brightening strategy of enhancing the brightness of image with minimum information loss. The MPC smartly blends the constrains of non-local prior, local prior, and global prior to shrink the solution space of haze removal, which avoids the limitation of using any single priors. Unlike previous works, IDBP does not require any training process, but is based on multiple priors and minimal information loss principle to impose the ASM, thereby making it easy to implement and ensuring its robustness. Numerous experiments reveal that the proposed IDBP outperforms the state-of-the-art alternates.

Internet of underwater things (IoUT) attracts many interests in these years both in academia and industry, such as marine data collection, pollution monitoring, and offshore exploration. As a fundamental issue of IoUT, the underwater acoustic channel experiences long delay and temporal-spatial uncertainty compared with terrestrial communications and networks. It is difficult to capture full characteristics of the underwater acoustic channel by statistical models. In this paper, we investigate the properties of acoustic propagation in seawater and different underwater acoustic channel models. Moreover, we survey five underwater acoustic channel models, including ray-theoretical model, normal mode model, multipath expansion model, fast-field model, and parabolic equation model, which are the corresponding solutions of the wave equation. We conclude the paper with the characteristics of each model in terms of different aspects.

In this work, a novel and ultra-robust single image dehazing method called IDRLP is proposed. It is observed that when an image is divided into n regions, with each region having a similar scene depth, the brightness of both the hazy image and its haze-free correspondence are positively related with the scene depth. Based on this observation, this work determines that the hazy input and its haze-free correspondence exhibit a quasi-linear relationship after performing this region segmentation, which is named as region line prior (RLP). By combining RLP and the atmospheric scattering model (ASM), a recovery formula (RF) can be easily obtained with only two unknown parameters, i.e., the slope of the linear function and the atmospheric light. A 2D joint optimization function considering two constraints is then designed to seek the solution of RF. Unlike other comparable works, this "joint optimization" strategy makes efficient use of the information across the entire image, leading to more accurate results with ultra-high robustness. Finally, a guided filter is introduced in RF to eliminate the adverse interference caused by the region segmentation. The proposed RLP and IDRLP are evaluated from various perspectives and compared with related state-of-the-art techniques. Extensive analysis verifies the superiority of IDRLP over state-of-the-art image dehazing techniques in terms of both the recovery quality and efficiency. A software release is available at https://sites.google.com/site/renwenqi888/.

Atmospheric scattering model (ASM) is one of the most widely used model to describe the imaging processing of hazy images. However, we found that ASM has an intrinsic limitation which leads to a dim effect in the recovered results. In this paper, by introducing a new parameter, i.e., light absorption coefficient, into ASM, an enhanced ASM (EASM) is attained, which can address the dim effect and better model outdoor hazy scenes. Relying on this EASM, a simple yet effective gray-world-assumption-based technique called IDE is then developed to enhance the visibility of hazy images. Experimental results show that IDE eliminates the dim effect and exhibits excellent dehazing performance. It is worth mentioning that IDE does not require any training process or extra information related to scene depth, which makes it very fast and robust. Moreover, the global stretch strategy used in IDE can effectively avoid some undesirable effects in recovery results, e.g., over-enhancement, over-saturation, and mist residue, etc. Comparison between the proposed IDE and other state-of-the-art techniques reveals the superiority of IDE in terms of both dehazing quality and efficiency over all the comparable techniques.

This paper introduces a novel and effective image prior, i.e., gamma correction prior (GCP), which leads to an efficient image dehazing method, i.e., IDGCP. A step-by-step procedure of the proposed IDGCP is as follows. First, an input hazy image is preprocessed by the proposed GCP, resulting in a homogeneous virtual transformation of the hazy image. Then, from the original input hazy image and its virtual transformation, the depth ratio is extracted based on atmospheric scattering theory. Finally, a "global-wise" strategy and a vision indicator are employed to recover the scene albedo, thus restoring the hazy image. Unlike other image dehazing methods, IDGCP is based on the "global-wise" strategy, and it only needs to determine one unknown constant without any refining process to attain a high-quality restoration, thereby leading to significantly reduced processing time and computation cost. Each step of IDGCP is tested experimentally to validate its robustness. Moreover, a series of experiments are conducted on a number of challenging images with IDGCP and other state-of-the-art technologies, demonstrating the superiority of IDGCP over the others in terms of restoration quality and implementation efficiency.

Atmospheric scattering model (ASM) has been widely used in hazy image restoration. However, the recovered albedo might deviate from the real scene once the input hazy image cannot fully satisfy the model's assumptions such as the homogeneous atmosphere and even illumination. In this paper, we break these limitations and redefine a more reliable ASM (RASM) that is extremely adaptable for various practical scenarios. Benefiting from RASM, a simple yet effective Bayesian dehazing algorithm (BDPK) is further proposed based on the prior knowledge. Our strategy is to convert the single image dehazing problem into a maximum a-posteriori probability one that can be approximated as an optimization function using the existing priori constraints. To efficiently solve this optimization function, the alternating minimizing technique is introduced, which enables us to directly restore the scene albedo. Experiments on a number of challenging images reveal the power of BDPK on removing haze and verify its superiority over several state-of-the-art techniques in terms of quality and efficiency.

In this letter, a concise gamma-correction-based dehazing model (GDM) is proposed. This GDM explicitly describes the inner relationship between the gamma correction (GC) and the traditional scattering model. Combined with the existing priori constraints, GDM is further approximated into a one-dimensional (1-D) function to seek the only unknown constant that is used for haze removal. Using the determined constant, the scene albedo can be recovered, eliminating the haze from single hazy images. The proposed GDM is able to suppress the halo/blocking artifacts in the recovered results due to the scene albedo, which is less sensitive to the determined constant. Simulation results on different types of benchmark images verify that the proposed technique outperforms state-of-the-art methods in terms of both recovery, quality, and real-time performance.

Under foggy or hazy weather conditions, the visibility and color fidelity of outdoor images are prone to degradation. Hazy images can be the cause of serious errors in many computer vision systems. Consequently, image haze removal has practical significance for real-world applications. In this study, we first analyze the inherent weaknesses of the atmospheric scattering model and propose an improvement to address those weaknesses. Then, we present a fast image haze removal algorithm based on the improved model. In our proposed method, the input image is partitioned into several scenes based on the haze thickness. Next, averaging and erosion operations calculate the rough scene luminance map in a scene-wise manner. We obtain the rough scene transmission map by maximizing the contrast in each scene and then develop a way to gently remove the haze using an adaptive method for adjusting scene transmission based on scene features. In addition, we propose a guided total variation model for edge optimization, so as to prevent from the block effect as well as to eliminate the negative effect from the wrong scene segmentation results. The experimental results demonstrate that our method is effective in solving a series of common problems, including uneven illuminance, overenhanced and oversaturated images, and so forth. Moreover, our method outperforms most current dehazing algorithms in terms of visual effects, universality, and processing speed.