Single-image dehazing plays a significant preprocessing role in machine vision tasks. As the dark-channel-prior method will fail in the sky region of the image, resulting in inaccurately estimated parameters, and given the failure of many methods to...
In this paper, we propose a novel single image enhancement technique for defogging by using dark channel prior. The traditional dark channel prior methods for defogging have problems of high time complexity, edge effect, and failure of dark channel p...
A sandstorm image has features similar to those of a hazy image with regard to the obtaining process. However, the difference between a sand dust image and a hazy image is the color channel balance. In general, a hazy image has no color cast and has...
The tolerance mechanism based on dark channel prior (DCP) of a single image dehazing algorithm is less effective when there are large areas of the bright region in the hazy image because it cannot obtain the tolerance adaptively according to the char...
For traditional dark channel prior (DCP) imaging through haze environments, intensity information acts as the carrier to acquire the reflective character of the dehazed target image. We introduce polarization as auxiliary information into the traditi...
Haze level evaluation is highly desired in outdoor scene monitoring applications. However, there are relatively few approaches available in this area. In this paper, a novel haze level evaluation strategy for real-world outdoor scenes is presented. T...
Sandstorm images have a color cast by sand particles. Hazy images have similar features to sandstorm images due to these images having a common obtaining process. To improve hazy images, various dehazing methods are being studied. However, not all me...
Compared with single-band remote sensing images, multispectral images can obtain information on the same target in different bands. By combining the characteristics of each band, we can obtain clearer enhanced images; therefore, we propose a multispe...
In fog, rain, snow, haze, and other complex environments, environmental objects photographed by imaging equipment are prone to image blurring, contrast degradation, and other problems. The decline in image quality fails to satisfy the requirements of...
Image dehazing has always been one of the main areas of research in image processing. The traditional dark channel prior algorithm (DCP) has some shortcomings, such as incomplete fog removal and excessively dark images. In order to obtain haze-free i...
To address the concerns of contrast deterioration, detail loss, and color distortion in images produced under haze conditions in scenarios such as intelligent driving and remote sensing detection, an algorithm for image defogging that combines Retine...
In order to restore traffic videos with different degrees of haziness in a real-time and adaptive manner, this paper presents an efficient traffic video dehazing method using adaptive dark channel prior and spatial-temporal correlations. This method...
In low illumination situations, insufficient light in the monitoring device results in poor visibility of effective information, which cannot meet practical applications. To overcome the above problems, a detail preserving low illumination video imag...
Foggy images suffer from low contrast and poor visibility problem along with little color information of the scene. It is imperative to remove fog from images as a pre-processing step in computer vision. The Dark Channel Prior (DCP) technique is a ve...
Recent advancements in artificial intelligence have led to significant improvements in object detection. Researchers have focused on enhancing the performance of object detection in challenging environments, as this has the potential to enhance pract...
Unpaired single-image dehazing has become a challenging research hotspot due to its wide application in modern transportation, remote sensing, and intelligent surveillance, among other applications. Recently, CycleGAN-based approaches have been popul...
Due to the detrimental impact of fog on image quality, dehazing maritime images is essential for applications such as safe maritime navigation, surveillance, environmental monitoring, and marine research. Traditional dehazing techniques, which are de...
Images captured in low-light conditions often have poor visibility, low contrast, and color distortion due to uneven lighting. Most existing enhancement methods often suffer from unstable brightness recovery and color cast, which affect both visual q...
Underwater images generally suffer from quality degradations, such as low contrast, color cast, blurring, and hazy effect due to light absorption and scattering in the water medium. In applying these images to various vision tasks, single image-based...
Commercial visibility sensors among meteorological sensors estimate the visibility distance based on transmission, backward scattering, and forward scattering principle. These optical visibility sensors yield comparatively accurate local visibility d...
Extreme weather conditions like fog and haze present substantial challenges to object recognition systems. Reduced visibility and contrast degradation significantly affect the auto-correlation process, often leading to failure in object recognition....
Image haze removal is essential in preprocessing for computer vision applications because outdoor images taken in adverse weather conditions such as fog or snow have poor visibility. This problem has been extensively studied in the literature, and th...
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 imag...
A duststorm image has a reddish or yellowish color cast. Though a duststorm image and a hazy image are obtained using the same process, a hazy image has no color distortion as it has not been disturbed by particles, but a duststorm image has color di...
There are many tasks that require clear and easily recognizable images in the field of underwater robotics and marine science, such as underwater target detection and identification of robot navigation and obstacle avoidance. However, water turbidity...
Image defogging is an essential technology used in traffic safety monitoring, military surveillance, satellite and remote sensing image processing, medical image diagnostics, and other applications. Current methods often rely on various priors, with...
Air quality monitoring in polluted environments is of great significance to human health. Traditional methods use various pieces of meteorological equipment, which have limited applications in complex terrains and high costs. In this paper, a novel i...
In a foggy traffic environment, the vision sensor signal of intelligent vehicles will be distorted, the outline of obstacles will become blurred, and the color information in the traffic road will be missing. To solve this problem, four ultra-fast de...
Since underwater imaging is affected by the complex water environment, it often leads to severe distortion of the underwater image. To improve the quality of underwater images, underwater image enhancement and restoration methods have been proposed....
Crab aquaculture is an important component of the freshwater aquaculture industry in China, encompassing an expansive farming area of over 6000 km2 nationwide. Currently, crab farmers rely on manually monitored feeding platforms to count the number a...
This study proposes real-time haze removal from a single image using normalised pixel-wise dark-channel prior (DCP). DCP assumes that at least one RGB colour channel within most local patches in a haze-free image has a low-intensity value. Since the...
This paper proposes a new single-image dehazing method, which is an important preprocessing step in vision applications to overcome the limitations of the conventional dark channel prior. The dark channel prior has a tendency to underestimate transmi...
Underwater image restoration is a challenging problem because light is attenuated by absorption and scattering in water, which can degrade the underwater image. To restore the underwater image and improve its contrast and color saturation, a novel al...
Recently, image haze removal has gained increasing attention in the field of image restoration. Data-driven and model-based methods are popular among researchers. The dark channel prior prevails in model-based methods, where the model parameters, atm...
The quality of underwater images is an important problem for resource detection. However, the light scattering and plankton in water can impact the quality of underwater images. In this paper, a novel underwater image restoration based on non-convex,...
In sandstorm environments, a large number of suspended particles in the air absorb and scatter light, causing strong color bias, low contrast, and blurred details in images. These degradations reduce the reliability of computer vision applications in...
Haze imagery suffers from reduced clarity, which can be attributed to atmospheric conditions such as dust or water vapor, resulting in blurred visuals and heightened brightness due to light scattering. Conventional methods employing the dark channel...
Traditional dehazing approaches that rely on prior knowledge exhibit limited efficacy when confronted with the intricacies of real-world hazy environments. While learning-based dehazing techniques necessitate large-scale datasets for effective model...
This study aims to develop a real-time gas leak detection system for application in gas cylinder filling machines. To promptly recover gas during leakage incidents, the efficiency of the gas filling process was improved by reducing resource wastage....
The dark channel prior (DCP)-based single image removal algorithm achieved excellent performance. However, due to the high complexity of the algorithm, it is difficult to satisfy the demands of real-time processing. In this article, we present a Grap...
This paper proposes a method and an original index for the estimation of fog density using images or videos. The proposed method had the advantages of convenient operation and low costs for applications in automatic driving and environmental monitori...
Traffic visibility is an essential reference for safe driving. Nighttime conditions add to the difficulty of estimating traffic visibility. To estimate the visibility in nighttime traffic images, we propose a Traffic Sensibility Visibility Estimation...
With the development of artificial intelligence technology, visual simultaneous localization and mapping (SLAM) has become a cheap and efficient localization method for underwater robots. However, there are many problems in underwater visual SLAM, su...
Low-light image enhancement (LLIE) is a fundamental task in computer vision, required for restoring luminance, contrast, and structural fidelity in images captured under suboptimal lighting environments. This paper introduces an optimization-driven,...
In this paper, we propose a lightweight U-net architecture neural network model based on Dark Channel Prior (DCP) for efficient haze (fog) removal with a single input. The existing DCP requires high computational complexity in its operation. These co...
As a result of human activity and environmental changes, several types of damages may occur to ancient mural paintings; indeed, lacunae, which refer to the area of paint layer loss, are the most prevalent kind. The presence of lacuna is an essential...
Surgical smoke generated by energy-based instruments during minimally invasive surgery severely degrades intraoperative visibility in laparoscopic procedures, prolonging operation time and elevating surgical risk. Although deep-learning desmoking met...
Image dehazing based on convolutional neural networks has achieved significant success; however, there are still some problems, such as incomplete dehazing, color deviation, and loss of detailed information. To address these issues, in this study, we...
To address the problems of low contrast, blurred target edges and strong background clutter interference in foggy maritime images, as well as the fixed parameters and poor adaptability of conventional enhancement methods in maritime scenarios, a weak...
Recently, supervised deep learning methods have been widely used for image haze removal. These methods rely on training data that are assumed to be appropriate. However, this assumption may not always be true. We observe that some data may contain ha...
of 3