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Community mask use is an efficacious non-pharmacologic way to minimize viral infection spread. It is a recommendation that individuals wear face masks as protective gear. Under ideal weather conditions, machine and artificial intelligence techniques can typically determine if a person is wearing a mask properly. Identification becomes more difficult under inclement weather such as fog, clouds, haze or rain. In this work, we propose a technique that can detect a human face wearing a mask even in adverse weather. For this, homogeneous foggy images have been considered. The main challenge with this problem is that video quality degrades because of fog. Here, diverse Deep learning models train regular datasets containing digital pictures of persons with facial and non-facial masks. The training and validation parameters ensure 97% accuracy in classifying faces wearing a mask. [ABSTRACT FROM AUTHOR]

In laparoscopic and thoracoscopic surgery, maintaining clear visualization is crucial for safe and efficient operation. We have developed a new scope warmer/cleaner (Lapahot®) based on "hot‐pack" technology. We used our device in 20 consecutive laparoscopic and thoracoscopic cases and compared it with the conventional warm saline method. Lens fogging was not observed in any of the cases, and the cleaning function was comparable to the warm saline method. Moreover, continuous temperature monitoring showed that Lapahot® maintained its temperature much longer than warm saline. Our novel device has the potential to become the new standard for lens defogging and cleaning. [ABSTRACT FROM AUTHOR]

Due to the increasing demand for artificial intelligence technology in today's society, the entire industrial production system is undergoing a transformative process related to automation, reliability, and robustness, seeking higher productivity and product competitiveness. Additionally, many hardware platforms are unable to deploy complex algorithms due to limited resources. To address these challenges, this paper proposes a computationally efficient lightweight convolutional neural network called Brightness Improved by Light-DehazeNet, which removes the impact of fog and haze to reconstruct clear images. Additionally, we introduce an efficient hardware accelerator architecture based on this network for deployment on low-resource platforms. Furthermore, we present a brightness visibility restoration method to prevent brightness loss in dehazed images. To evaluate the performance of our method, extensive experiments were conducted, comparing it with various traditional and deep learning-based methods, including images with artificial synthesis and natural blur. The experimental results demonstrate that our proposed method excels in dehazing ability, outperforming other methods in comprehensive comparisons. Moreover, it achieves rapid processing speeds, with a maximum frame rate of 105 frames per second, meeting the requirements of real-time processing. [ABSTRACT FROM AUTHOR]

Under a foggy environment, the air contains a large number of suspended particles, which lead to the loss of image information and decline of contrast collected by the vision system. This makes subsequent processing and analysis difficult. At the same time, the current stage of the defogging system has problems such as high hardware cost and poor real-time processing. In this article, an image defogging system is designed based on the ZYNQ platform. First of all, on the basis of the traditional dark-channel defogging algorithm, an algorithm for segmenting the sky is proposed, and in this way, the image distortion caused by the sky region is avoided, and the atmospheric light value and transmittance are estimated more accurately. Then color balancing is performed after image defogging to improve the quality of the final output image. The parallel computing advantage and logic resources of the PL (Programmable Logic) part (FPGA) of ZYNQ are fully utilized through instruction constraints and logic optimization. Finally, the visible light detector is used as the input to build a real-time video processing experiment platform. The experimental results show that the system has a good defogging effect and meet the real-time requirements. [ABSTRACT FROM AUTHOR]

In the field of traffic surveillance systems, where effective traffic management and safety are the primary concerns, vehicle detection and tracking play an important role. Low brightness, low contrast, and noise are issues with low-light environments that result from poor lighting or insufficient exposure. In this paper, we proposed a vehicle detection and tracking model based on the aerial image captured during nighttime. Before object detection, we performed fogging and image enhancement using MIRNet architecture. After pre-processing, YOLOv5 was used to locate each vehicle position in the image. Each detected vehicle was subjected to a Scale-Invariant Feature Transform (SIFT) feature extraction algorithm to assign a unique identifier to track multiple vehicles in the image frames. To get the best possible location of vehicles in the succeeding frames templates were extracted and template matching was performed. The proposed model achieves a precision score of 0.924 for detection and 0.861 for tracking with the Unmanned Aerial Vehicle Benchmark Object Detection and Tracking (UAVDT) dataset, 0.904 for detection, and 0.833 for tracking with the Vision Meets Drone Single Object-Tracking (VisDrone) dataset.

We proposed a novel image-enhancing framework to ensure consolidated restoration accuracy when remedying the visual quality of dehazed images, such as over-saturation, color deviation, or luminance issues. Conventionally, the dehazing process was usually considered image restoration; however, enhancement methods only aimed to improve dehazed images’ contrast, brightness, and detail. Therefore, the conventional enhancing framework tended to degrade consolidated restoration accuracy due to focusing on low-level image features rather than controlling errors associated with the dehazing process. Our method aimed to improve the consolidated restoration accuracy of the dehazing-and-enhancing process. In experiments, the proposed framework improved the visual quality and preserved restoration accuracy (despite enhancements) with high computational efficiency and resolved quality issues generated by conventional dehazing algorithms. Moreover, the minimal time complexity of the proposed framework is O(n), ensuring practical applicability when implemented in conjunction with state-of-the-art dehazing algorithms.

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In recent years, the utilization of deep learning for forest fire detection has yielded favorable outcomes. Nevertheless, the accurate segmentation of forest fires in foggy surroundings with limited visibility remains a formidable obstacle. To overcome this challenge, a collaborative defogging learning framework, known as Defog DeepLabV3+, predicated on an enhanced DeepLabV3+ model is presented. Improved learning and precise flame segmentation are accomplished by merging the defogging features produced by the defogging branch in the input image. Furthermore, dual fusion attention residual feature attention (DARA) is proposed to enhance the extraction of flame-related features. The FFLAD dataset was developed given the scarcity of specifically tailored datasets for flame recognition in foggy environments. The experimental findings attest to the efficacy of our model, with a Mean Precision Accuracy (mPA) of 94.26%, a mean recall (mRecall) of 94.04%, and a mean intersection over union (mIoU) of 89.51%. These results demonstrate improvements of 2.99%, 3.89%, and 5.22% respectively. The findings reveal that the suggested model exhibits exceptional accuracy in foggy conditions, surpassing other existing models across all evaluation metrics. [ABSTRACT FROM AUTHOR]

Under a foggy environment, the air contains a large number of suspended particles, which lead to the loss of image information and decline of contrast collected by the vision system. This makes subsequent processing and analysis difficult. At the same time, the current stage of the defogging system has problems such as high hardware cost and poor real-time processing. In this article, an image defogging system is designed based on the ZYNQ platform. First of all, on the basis of the traditional dark-channel defogging algorithm, an algorithm for segmenting the sky is proposed, and in this way, the image distortion caused by the sky region is avoided, and the atmospheric light value and transmittance are estimated more accurately. Then color balancing is performed after image defogging to improve the quality of the final output image. The parallel computing advantage and logic resources of the PL (Programmable Logic) part (FPGA) of ZYNQ are fully utilized through instruction constraints and logic optimization. Finally, the visible light detector is used as the input to build a real-time video processing experiment platform. The experimental results show that the system has a good defogging effect and meet the real-time requirements.

Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The images which are taken in foggy weather/bad weather are not expressed clearly therefore they are not very comprehensible. The images that are taken in that environment will be reduced in quality to a great degree, the reason is that because of the scattering of light in the atmosphere will reduce the clarity and visibility of a single image. Because of fog, the images are in low contrast whereas in case the haze in the atmosphere will affect the clearness and precision of the image. The pictures taken in this condition will not only affect the visibility but also complicate the post-processing of the image required for the implementation of numerous computer vision algorithms. Hence it is important to eliminate fog from the image So, firstly density of fog is estimated and based on that, image defogging can be done. [ABSTRACT FROM AUTHOR]

In recent years, the utilization of deep learning for forest fire detection has yielded favorable outcomes. Nevertheless, the accurate segmentation of forest fires in foggy surroundings with limited visibility remains a formidable obstacle. To overcome this challenge, a collaborative defogging learning framework, known as Defog DeepLabV3+, predicated on an enhanced DeepLabV3+ model is presented. Improved learning and precise flame segmentation are accomplished by merging the defogging features produced by the defogging branch in the input image. Furthermore, dual fusion attention residual feature attention (DARA) is proposed to enhance the extraction of flame-related features. The FFLAD dataset was developed given the scarcity of specifically tailored datasets for flame recognition in foggy environments. The experimental findings attest to the efficacy of our model, with a Mean Precision Accuracy (mPA) of 94.26%, a mean recall (mRecall) of 94.04%, and a mean intersection over union (mIoU) of 89.51%. These results demonstrate improvements of 2.99%, 3.89%, and 5.22% respectively. The findings reveal that the suggested model exhibits exceptional accuracy in foggy conditions, surpassing other existing models across all evaluation metrics.

Images captured in foggy weather conditions suffers from inevitable problems such as low contrast, blurriness and less visibility. In many computer and vision applications like surveillance, object detection, object tracking and navigation, these low-quality images cannot be used and it requires improvement in the image quality. Different algorithms have been proposed in this direction and to upgrade the quality of a foggy image. Most of the existing methods gives good quality image but with high time complexity. In this paper, a novel and effective method is proposed to remove fog from a single image. The proposed method is based on principal component analysis and modified dark channel prior. In proposed algorithm, foggy image is pre-processed using principal component analysis. This pre-processed image is further enhanced using fast global smoothening filter. Time complexity of the proposed method is much less as compared to the various existing methods and at the same time, quality is also maintained. Also, proposed algorithm does not require a large data set and specific hardware. To see the effectiveness of the proposed technique, both qualitative and quantitative analysis has been done on synthetic data set as well as on natural dataset.

Polar cruise ships are exposed to extreme external conditions during voyages, resulting in cockpit windscreens that are prone to fogging and frosting, seriously affecting the driver's vision and even threatening navigation safety. However, the current research mainly focuses on cabin comfort, ignoring the coupling of defogging and comfort. Accordingly, this paper combines cockpit-windshield-defogging design and cockpit comfort considerations, and proposes 108 orthogonal-ventilation design parameters based on the four basic ventilation methods. The effects of different air supply parameters on comfort and anti-fog characteristics are investigated by using fluid dynamics simulation methods. The entropy weight–TOPSIS algorithm is employed to find the optimal ventilation parameters. The results show that the "Down-supply up-return type vertical jet" air supply method corresponding to an air supply velocity of 1 m/s, an air supply temperature of 297 K, and an air supply relative humidity of 30% has the smallest Euclidean distance d i + from the positive ideal solution, and the largest Euclidean distance d i − from the negative ideal solution; thus, it obtains a higher c i and the highest priority. This air supply method provides the best thermal comfort for the drivers, as well as the best anti-fogging and defogging effect. The results can be useful to provide suggestions for the future design of the air-conditioning systems in polar cruise ships. [ABSTRACT FROM AUTHOR]

Opencast mining operations at hilly areas are usually affected during foggy weather due to the inability of drivers to operate heavy earth‐moving machinery in low visibility conditions. This article deals with an intelligent vision enhancement system for continuing opencast mining operations during foggy weather. The system integrates hardware and software to provide multistage safety features that make it unique from existing systems. The system includes hardware like thermal cameras, high definition cameras, proximity radar, wireless devices, GNSS module, graphical processing unit, display unit, and so forth, and image processing software, namely real‐time image stitching, image enhancement, and object detection using convolutional neural networks. The integrated system and algorithms display a 180° panorama field view of the vehicle's front using real‐time video stitching. The front view after image processing, rear camera view, object detection through proximity radar, and real‐time location of the vehicles on a 3D geo‐tagged mine map by GNSS modules are displayed in four splitter windows on a touch screen fitted on the dashboard in front of the driver's seat. The driver can drive the vehicle by seeing the display screen during foggy weather. The output image of the developed image‐processing algorithm has less distortion, better quality, and better depth perception than existing methods. Overall, there are significant improvements in the persistence of the color elements by 39.65%, contrast by 4.62%, and the corresponding entropy by 7.11% concerning the similar existing methods. The final system has been successfully tested in an opencast mine. [ABSTRACT FROM AUTHOR]

During the imaging process, the image will be polluted by noise due to the influence of the earth's atmosphere fog. Noise pollution directly affects the processing of feature extraction, image segmentation, and pattern recognition. Therefore, the purpose of image defogging is to eliminate noise from foggy images to improve image quality. When dealing with uneven fog density in the same image, most of the existing methods are not satisfactory. Integrated image defogging network based on improved atmospheric scattering model and attention feature fusion is proposed. In order to distinguish between the presence of fog with different concentrations in the image. We adopt the concept of attention that has been important in convolutional neural networks (CNN) in recent years. We adopt the attention mechanism to help our network pay more attention to important feature information, treat thick fog and mist differently, and solve the problem of uneven distribution of fog in the image, but the network handles the problem with the same weight. Finally, use a five-layer convolution operation to restore the image. The experimental results show that our method achieves a good defogging effect on the uneven distribution of the fog density of the synthesized foggy image. In the experiment of real natural foggy images, relying on the attention mechanism network to remove the fog, serious patches will appear on the restored images. Our method will not damage the image. Our method can reasonably remove thick fog and mist to obtain a good defogging effect. And our proposed method is robust. [ABSTRACT FROM AUTHOR]

Abstract Image acquisition under bad weather conditions is prone to yield image with low contrast, faded color, and overall poor visibility. Different computer vision applications including surveillance, object classification, tracking, and recognition get effected due to degraded hazy images. Dehazing can significantly improve contrast, balance luminance, correct distortion, remove unwanted visual effects/ and therefore enhance the image quality. As a result, image defogging is imperative pre-processing step in computer vision applications. Previously, dark channel prior-based algorithms have proven promising results over the available techniques. In this paper, we have proposed a modified dark channel prior that uses fog density and guided image-filtering technique to estimate and refine transmission map, respectively. Guided image filter speeds up the refinement of transmission map, hence reduces the overall computational complexity of algorithm. We have also incorporated segmentation of the foggy image into sky and non-sky regions, after which, the modified dark channel prior and atmospheric light is computed for each segment. Then, the average value of atmospheric light for each segment is used to estimate transmission map. We have performed quantitative and subjective comparison for effective evaluation of our proposed algorithm against the current state-of-the-art algorithms on natural and synthetic images. Different quality metrics, such as saturation, mean square error, fog density, peak signal to noise ratio, structural similarity index metric, dehazing algorithm index (DHQI), full-reference image quality assessmen (FR-IQA), and naturalness of dehazed images have shown the proposed algorithm to be better than existing techniques.

Haze is a source of unreliability for computer vision applications in outdoor scenarios, and it is usually caused by atmospheric conditions. The Dark Channel Prior (DCP) has shown remarkable results in image defogging with three main limitations: 1) high time-consumption, 2) artifact generation, and 3) sky-region over-saturation. Therefore, current work has focused on improving processing time without losing restoration quality and avoiding image artifacts during image defogging. Hence in this research, a novel methodology based on depth approximations through DCP, local Shannon entropy, and Fast Guided Filter is proposed for reducing artifacts and improving image recovery on sky regions with low computation time. The proposed-method performance is assessed using more than 500 images from three datasets: Hybrid Subjective Testing Set from Realistic Single Image Dehazing (HSTS-RESIDE), the Synthetic Objective Testing Set from RESIDE (SOTS-RESIDE) and the HazeRD. Experimental results demonstrate that the proposed approach has an outstanding performance over state-of-the-art methods in reviewed literature, which is validated qualitatively and quantitatively through Peak Signal-to-Noise Ratio (PSNR), Naturalness Image Quality Evaluator (NIQE) and Structural SIMilarity (SSIM) index on retrieved images, considering different visual ranges, under distinct illumination and contrast conditions. Analyzing images with various resolutions, the method proposed in this work shows the lowest processing time under similar software and hardware conditions.

Imaging of unmanned aerial vehicle easily suffer from haze, resulting in decline in the quality of required remote sensing images. The influence brings great challenges in later analysis and process. Although dark channel prior has acquired substantial achievements, some limitations, including imprecise estimation of atmospheric light, color distortion, and lower brightness of defogging image, still exist. In this article, to target these drawbacks, a novel defogging method for single image is proposed. First, a novel atmospheric scattering model is proposed to define the more accurate atmospheric light by introducing an adaptive variable strategy. Next, unlike traditional dark channel prior, a novel estimation method is presented by fusing dark and light channels to estimate more precise atmospheric light and transmittance. Then, we adopt the gray image corresponding to color image as a guidance image to refine the transmittance to further decrease the time complexity. Aiming at the region of low transmittance, a novel compensation function is created to improve the region of low transmittance and avoid color distortion. Moreover, a simple and effective calculation method is proposed to determine parameters in compensation function. Finally, the clear remote sensing image is established by an improved atmospheric scattering model. Extensive experiments on real-world datasets demonstrate that the proposed method outperforms several other state-of-the-art approaches both on subjective and objective quality evaluations.

Real-time video defogging has a huge demand in intelligent transportation, advanced driver assistance systems (ADAS), long-range surveillance, autonomous aerial vehicles and endoscopic surgery. Most of these applications are constrained by stringent frame rate, power and memory budget. Till date, the methods devised for such requirements are very rare. Therefore, this paper proposes an efficient method and very-large-scale integration (VLSI) architecture for resource-constrained embedded system targeting real-time video defogging. The method and architecture are co-designed to achieve high throughput while consuming less resources and power. The architecture is divided into four parts namely, atmospheric light estimation unit, airlight adjustment unit, transmission estimation unit, and pixel restoration unit. The atmospheric light estimation unit employs a $3\times 3$ tile based approach that eliminates the requirement of large buffer memory for $15\times 15$ support size. The transmission is estimated using dark channel prior and gradient threshold based Gaussian filtering approach. In order to overcome flickering artifacts, an adaptive airlight updating scheme is employed. From the quantitative and qualitative evaluations, it is observed that the proposed method outperforms the existing hardware approaches. Furthermore, the field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) implementations of the architecture achieve a high throughput of 200 MPixels/s and 600 MPixels/s, respectively. The ASIC implementation dissipates 9.03 mW power at 200MHz. Moreover, the proposed design does not require any external memory such as dynamic random-access memory (DRAM), thus making it suitable for on-chip processing that can be closely integrated with an image sensor. [ABSTRACT FROM AUTHOR]

Haze and fog had a great influence on the quality of images, and to eliminate this, dehazing and defogging are applied. For this purpose, an effective and automatic dehazing method is proposed. To dehaze a hazy image, we need to estimate two important parameters such as atmospheric light and transmission map. For atmospheric light estimation, the superpixels segmentation method is used to segment the input image. Then each superpixel intensities are summed and further compared with each superpixel individually to extract the maximum intense superpixel. Extracting the maximum intense superpixel from the outdoor hazy image automatically selects the hazy region (atmospheric light). Thus, we considered the individual channel intensities of the extracted maximum intense superpixel as an atmospheric light for our proposed algorithm. Secondly, on the basis of measured atmospheric light, an initial transmission map is estimated. The transmission map is further refined through a rolling guidance filter that preserves much of the image information such as textures, structures and edges in the final dehazed output. Finally, the haze-free image is produced by integrating the atmospheric light and refined transmission with the haze imaging model. Through detailed experimentation on several publicly available datasets, we showed that the proposed model achieved higher accuracy and can restore high-quality dehazed images as compared to the state-of-the-art models. The proposed model could be deployed as a real-time application for real-time image processing, real-time remote sensing images, real-time underwater images enhancement, video-guided transportation, outdoor surveillance, and auto-driver backed systems. [ABSTRACT FROM AUTHOR]

Single image dehazing (SID) solves the atmospheric scattering model (ATSM). The ill-defined nature of the SID makes it a challenging problem. The transmission is the prime parameter of ATSM. Hence, accurate transmission is essential for quality of SID. The existing methods of SID estimate the transmission based on priors with strong assumptions (such as dark channel prior). These methods do not recover original colors, structure and visibility due to wrong transmission under invalidity of these assumptions. Therefor, the difference channel (DCH) is proposed to estimate accurate transmission. The DCH non-linearly translates the minimum channel of hazy image into minimum channel of haze-free image, which is used to compute the value of transmission. The DCH is based on an observation that difference of maximum and minimum color channel of the hazy image is negatively correlated with depth. The proposed method is able to recover the details from hazy image in the form of structure, edges, corners, colors and visibility due to the DCH. The accuracy and robustness of the proposed method is proved by comparing the results with known dehazing methods based on qualitative and quantitative analysis using benchmark data sets. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Outdoor images can be degraded due to the particles in the air that absorb and scatter light. The produced degradation generates contrast attenuation, blurring, and distortion in pixels, resulting in low visibility. These limit the efficiency of computer vision systems such as target tracking, surveillance, and pattern recognition. In this paper, we propose a fast and effective method, through modification in the computation of the dark channel which significantly reduces the artifacts generated in the restored images presented when using the ordinary dark channel. According to our experimental results, our method produces better results than some state-of-the-art methods in both efficiency and restoration quality. The processing time in tests shows that the method is adequate for images with high-resolution and real-time video processing.

Polar cruise ships are exposed to extreme external conditions during voyages, resulting in cockpit windscreens that are prone to fogging and frosting, seriously affecting the driver’s vision and even threatening navigation safety. However, the current research mainly focuses on cabin comfort, ignoring the coupling of defogging and comfort. Accordingly, this paper combines cockpit-windshield-defogging design and cockpit comfort considerations, and proposes 108 orthogonal-ventilation design parameters based on the four basic ventilation methods. The effects of different air supply parameters on comfort and anti-fog characteristics are investigated by using fluid dynamics simulation methods. The entropy weight–TOPSIS algorithm is employed to find the optimal ventilation parameters. The results show that the “Down-supply up-return type vertical jet” air supply method corresponding to an air supply velocity of 1 m/s, an air supply temperature of 297 K, and an air supply relative humidity of 30% has the smallest Euclidean distance di+ from the positive ideal solution, and the largest Euclidean distance di− from the negative ideal solution; thus, it obtains a higher ci and the highest priority. This air supply method provides the best thermal comfort for the drivers, as well as the best anti-fogging and defogging effect. The results can be useful to provide suggestions for the future design of the air-conditioning systems in polar cruise ships.

Aiming at the problem that the edge preserving effect of the existing improved bilateral filtering algorithm in the process of dealing with the haze weather traffic images is not ideal, and the algorithm is time consuming and computationally inefficient, which is unacceptable in the traffic video defogging. Thus, this paper proposed a new algorithm based on median guide filter for this target. In the full paper, we explain in some detail about median guide filter; in this abstract, we just add some pertinent remarks to listing the title of each section of this paper. The six sections of this paper are:introduction(section 0), traffic image degradation model in fog and haze weather(section 1), guide filter(section 2), the design of median guide filter(section 3), Optimization of transmittance estimation(section 4), experimental result(section 5), conclusion(section 6). In section 2, we introduce the basic principle of guide filter. In section 3, we propose median guide filter and the formulas of algorithm are deduced and point out the deficiency of the traditional algorithm. In section 4, we introduce a simple method to optimize transmittance estimation. In section 5, In order to verify the effect of defog, we perform a simulation on MATLAB, the analyze of simulation results, indicates that our median guide filter algorithm can not only guarantee the effectiveness of defog, but also ensure the efficiency of calculation.

Atmospheric conditions induced by suspended particles such as fog, smog, rain, haze etc., severely affect the scene appearance and computer vision applications. In general, existing defogging algorithms use various constraints for fog removal. The efficiency of these algorithms depends on the accurate estimation of the depth models and the perfection of these models solely relies on pre-calculated coefficients through the training data. However, the depth model developed on the basis of these pre-calculated coefficients for dehazing may provide better accuracy for some kind of images but not equally well for every type of images. Therefore, training data-independent based depth model is required for a perfect haze removal algorithm. In this paper, an effective haze removal algorithm is reported for removing fog or haze from a single image. The proposed algorithm utilizes the atmospheric scattering model in fog removal. Apart from this, linearity in the depth model is achieved by the ratio of difference and sum of the intensity and saturation values of the input image. Besides, the proposed method also take care the well-known problems of edge preservation, white region handling and colour fidelity. Experimental results show that the proposed model is more efficient in comparison to the existing haze removal algorithms in terms of qualitative and quantitative analysis. [ABSTRACT FROM AUTHOR]

Fog on the automotive windshield interferes with the sight of the driver and causes serious safety problems; thus, removing it rapidly has been a longstanding goal in the automobile industry. In this study, we propose a novel method for improving the defogging performance of the defroster without its structural alteration by using vortex generators (VGs). To optimize the performance of the VGs, we performed particle image velocimetry experiments, an evaporation measurement experiment, and a numerical simulation for visualizing the airflow from the defroster inlet. The dimensions of the VG (height h = δ and length ℓ = 5δ) were selected according to the boundary-layer thickness, δ, of the lowest-flow rate (ulow = 0.33 m/s) used in the defroster inlet. We employed a dimensionless parameter, i.e., the secondary flow intensity (Se), to quantify the intensity of vortices for several angles of attack (30°, 45°, and 60°). The results indicated that the vortices generated from VGs with a 30° angle of attack retained their maximum intensity until they reached the vision area of the driver. Additionally, we explored two different configurations of VGs—co-rotating and counter-rotating—and concluded that the counter-rotating configuration had higher performance than the co-rotating configuration. The optimized VGs were directly inserted in the defroster inlet and improved the defogging performance by approximately 10%. The proposed method is applicable to various automobile models for enhancing the performance of the defroster regardless of the interior volume, air-ventilation performance, and other conditions, such as the number of passengers and outside temperature. [ABSTRACT FROM AUTHOR]

The visibility of an image captured in poor weather (such as haze, fog, mist, smog) degrades due to scattering of light by atmospheric particles. Single image dehazing (SID) methods are used to restore visibility from a single hazy image. The SID is a challenging problem due to its ill-posed nature. Typically, the atmospheric scattering model (ATSM) is used to solve SID problem. The transmission and atmospheric light are two prime parameters of ATSM. The accuracy and effectiveness of SID depends on accurate value of transmission and atmospheric light. The proposed method translates transmission estimation problem into estimation of the difference between minimum color channel of hazy and haze-free image. The translated problem presents a lower bound on transmission and is used to minimize reconstruction error in dehazing. The lower bound depends upon the bounding function (BF) and a quality control parameter. A non-linear model is then proposed to estimate BF for accurate estimation of transmission. The proposed quality control parameter can be utilized to tune the effect of dehazing. The accuracy obtained by the proposed method for transmission is compared with state of the art dehazing methods. Visual comparison of dehazed images and objective evaluation further validates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Image acquisition under bad weather conditions is prone to yield image with low contrast, faded color, and overall poor visibility. Different computer vision applications including surveillance, object classification, tracking, and recognition get effected due to degraded hazy images. Dehazing can significantly improve contrast, balance luminance, correct distortion, remove unwanted visual effects/ and therefore enhance the image quality. As a result, image defogging is imperative pre-processing step in computer vision applications. Previously, dark channel prior-based algorithms have proven promising results over the available techniques. In this paper, we have proposed a modified dark channel prior that uses fog density and guided image-filtering technique to estimate and refine transmission map, respectively. Guided image filter speeds up the refinement of transmission map, hence reduces the overall computational complexity of algorithm. We have also incorporated segmentation of the foggy image into sky and non-sky regions, after which, the modified dark channel prior and atmospheric light is computed for each segment. Then, the average value of atmospheric light for each segment is used to estimate transmission map. We have performed quantitative and subjective comparison for effective evaluation of our proposed algorithm against the current state-of-the-art algorithms on natural and synthetic images. Different quality metrics, such as saturation, mean square error, fog density, peak signal to noise ratio, structural similarity index metric, dehazing algorithm index (DHQI), full-reference image quality assessmen (FR-IQA), and naturalness of dehazed images have shown the proposed algorithm to be better than existing techniques. [ABSTRACT FROM AUTHOR]

Effective functioning of outdoor vision systems depends upon the quality of input. Varying effects of light create different weather conditions (like raining, snowfall, haze, mist, fog, and cloud) due to optical properties of light and physical existence of different size particles in the atmosphere. Thus, outdoor images and videos captured in adverse environmental conditions have poor visibility due to scattering of light by atmospheric particles. Visibility restoration (dehazing) of degraded (hazy) images is critical for the useful performance of outdoor vision systems. Most of the existing methods of image dehazing considered atmospheric scattering model (ASM) to improve the visibility of hazy images or videos. According to ASM, the visual quality of dehazed image depends upon accurate estimation of transmission. Existing methods presented different priors with strong assumptions to estimate transmission. The proposed method introduces a tight lower bound on transmission. However, the accuracy of the proposed tight lower bound depends upon minimum color channel of haze-free image. Therefore, a prior is proposed to estimate the minimum color channel of the haze-free image. Furthermore, a blind assessment metric is proposed to evaluate the dehazing methods. Restored and matching corner points of the hazy and haze-free image are used to compute the proposed blind assessment metric. Obtained results are compared with renowned dehazing methods by qualitative and quantitative analysis to prove the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

Fog is the most hindrance and unavoidable problem faced by drivers while driving. Due to foggy condition and poor visibility, especially in early morning and late-night, drivers are unable to see distant object on the road. As a result, possibility of road accident increases. In this article, a fast-real-time vision-based defogging system is proposed to clear the vision of highway during driving in the foggy environmental condition. The proposed system can remove the haziness of the driver’s vision and can present a clear view of the road within a very short span of time. Processing of each frame is comprised of four steps: calculation of atmospheric light using minimum filter, transmission map, scene radiance and finally gamma correction is applied for removing the haziness with perfect contrast adjustment. In order to reduce time complexity, instead of estimating atmospheric light for each frame, it is calculated at an interval of 5000 frames. Many real-time heuristic tests have been conducted during day as well at night on the highway and test analysis reveals that, after defogging, the distance of visibility increases by more than 65% during heavy fog. Besides, there is a massive increase in visibility during low foggy condition also. [ABSTRACT FROM AUTHOR]

The single image dehazing is performed using atmospheric scattering model (ASM). The ASM is based on transmission and atmospheric light. Thus, accurate estimation of transmission is essential for quality single image dehazing. Single image dehazing is of prime focus in research nowadays. The proposed work presents a fast and accurate method for single image dehazing. The proposed method works in two folds; (i) An adaptive dehazing control factor is proposed to estimate accurate transmission, which is based on difference of maximum and minimum color channel of hazy image, and (ii) a mathematical model to compute probability of a pixel to be at short distance is presented, which is utilized to locate haziest region of the image to compute the value of atmospheric light. The proposed method obtains visually compelling results, and recovers the information content (such as structural similarity, color, and visibility) accurately. The computation speed and accuracy of the proposed method is proved using quantitative and qualitative comparison of results with state of the art dehazing methods. [ABSTRACT FROM AUTHOR]

Image fusion and reconstruction from muldti-images taken by distributed or mobile cameras need accurate calibration to avoid image mismatching. This calibration process becomes difficult in fog when no clear nearby reference is available. In this work, the fusion of multi-view images taken in fog by two cameras fixed on a moving platform is realized. The positions and aiming directions of the cameras are determined by taking a close visible object as a reference. One camera with a large field of view (FOV) is applied to acquire images of a short-distance object which is still visible in fog. This reference is then adopted to the calibration of the camera system to determine the positions and pointing directions at each viewpoint. The extrinsic parameter matrices are obtained with these data, which are applied for the image fusion of distant images captured by another camera beyond visibility. The experimental verification was carried out in a fog chamber and the technique is shown to be valid for imaging reconstruction in fog without a prior in-plane. The synthetic image, accumulated and averaged by ten-view images, is shown to perform potential applicability for fog removal. The enhanced structure similarity is discussed and compared in detail with conventional single-view defogging techniques.

The evolving need for all-weather light detection and ranging (LiDAR) sensors and cameras for autonomous vehicles, remote sensing surveillance, and space exploration has spurred the development of transparent heaters. While LiDAR photon sources have shifted from the visible to the near-infrared (NIR) range, the use of transparent conductive oxides (TCOs) for heaters leads to significant optical losses due to their high plasmonic absorption and reflection in the NIR range. Although different TCO compositions can be employed to preserve transparency and electrical conductivity in this range, the choice of dopants, their concentrations, and the underlying mechanisms remain largely unknown. In this study, we present TCOs specifically designed for NIR applications with a focus on identifying new compositions that strike a balance between NIR transparency and electrical conductivity. We present a 4B-6B transition-metal-doped indium oxide thin-film heater that exhibits impressive NIR transmittance (>90%) surpassing that of commonly used indium tin oxide films. By incorporating effective dopants such as titanium, hafnium, and tungsten, we successfully reduced the resistivity and enhanced the electrical conductivity of indium oxide films. To enhance the practical utility of the film, we implemented post-treatments comprising argon plasma treatment and encapsulation with low-molecular-weight poly(dimethylsiloxane), which resulted in significantly improved performance. The optimized film exhibited a sheet resistance of 520 Ω/sq and excellent optical transmittance at 850 nm (89.1%), 905 nm (89.7%), and 1550 nm (92%). Moreover, we successfully integrated defogging and defrosting capabilities into a commercial LiDAR camera and demonstrated its reliable operation in challenging environments.

In this paper, we present a novel defogging technique, named CurL-Defog, with the aim of minimizing the insertion of artifacts while maintaining good contrast restoration and visibility enhancement. Many learning-based defogging approaches rely on paired data, where fog is artificially added to clear images; this usually provides good results on mildly fogged images but is not effective for difficult cases. On the other hand, the models trained with real data can produce visually impressive results, but unwanted artifacts are often present. We propose a curriculum learning strategy and an enhanced CycleGAN model to reduce the number of produced artifacts, where both synthetic and real data are used in the training procedure. We also introduce a new metric, called HArD (Hazy Artifact Detector), to numerically quantify the number of artifacts in the defogged images, thus avoiding the tedious and subjective manual inspection of the results. HArD is then combined with other defogging indicators to produce a solid metric that is not deceived by the presence of artifacts. The proposed approach compares favorably with state-of-the-art techniques on both real and synthetic datasets.

It is necessary to perform fog removal from an image based on the estimation of depth to increase the visibility of a scene. In this paper, we propose a new algorithm to eradicate fog from images in which fog is defined as a state or cause of perplexity or confusion with respect to the image. It runs at high speed and simultaneously minimizes the halo-artifact with a new median operator in dark channel prior. The proposed method is based on Guided Filter for transmission-map refinement and Contrast Limited Adaptive Histogram Equalization (CLAHE) for visibility improvement. It preserves small details while remaining robust against density of fog, and recovers scene contrast simultaneously. Guided filter improved the transmission map acquired from Median dark channel prior (MDCP), which is an improvement of the Dark Channel Prior DCP by the use of median operation. All of the parameters used in our method are data driven. The quality of algorithm has been validated on several types of fog-degraded images where considerable variation in contrast and illumination exists. Moreover, its performance is compared with the other state-of-the-art methods. The experimental results indicate that the proposed method effectively restores the color and contrast of scene as well as produces satisfactory information in homogeneous fog. It outperforms the existing fog removal methods for run time computational time and other evaluation metrics for rating of visibility enhancement. The proposed method conserves small details part of the image when outstanding vigorous against concentration of fog, and recuperate scene contrast instantaneously. It controls at a high speed than the existing approaches and can diminish the halo effect. [ABSTRACT FROM AUTHOR]

Capturing the image in fog scene suffers from the distortions of the information and the time taken to predict the object in the way become complicated. To overcome this difficulty, the images are taken into consideration as new a technology opens door for converting the analog scene to digital scene in the form of an image. The image captured in less visibility of the scene predominantly in foggy weather conditions, the structure of image, and also several human activities like drones, aircrafts, flights, and travellers affect several computer vision applications like tracking, artificial intelligence, remote sensing, etc. Thus restoring back the outdoor scene from such foggy image is significantly important. The main focus is to defog the image in the patch; atmospheric light in foggy days looks as that of the fog, so this to be reduced; edges and corners must be visible, inner depths to be reconstructed from the fog scene. To fulfill these characteristics of image, several defog techniques were investigated. The Spatial Random Markov Fields with Boundary Constraints was proposed, which performs on image within the space and patch using boundary constraints. Experimental results demonstrate that the proposed work is efficient to remove fog, restore space of an image, and preserve the natural atmospheric light even in foggy days without changing the color. [ABSTRACT FROM AUTHOR]