Sky Segmentation Research Articles

Traffic image dehazing based on sky region segmentation and transmittance optimization

Traffic sign recognition is of great significance to promote traffic sustainability and maintain traffic safety. GPS monitoring systems and advanced autonomous vehicles are often heavily reliant on camera imagery. Algorithms based on dark channel prior are susceptible to color distortion when processing traffic images containing bright sky or high-brightness areas, which can negatively impact the identification of traffic signals and signage located in elevated positions. To address this issue, this paper proposes a dehazing algorithm (SRSTO) that combines sky region segmentation and transmittance optimization. Firstly, the gradient, brightness and saturation information are calculated, followed by the construction of a threshold function used in area segmentation. This approach is utilized to partition the image into areas not containing sky highlights and the area that contains them. Subsequently, the dark channel images of the sky and the non-sky regions are acquired, morphological operations are further performed in layers and blocks, and then the atmospheric scattered light value is calculated. Secondly, the functional relationship between the transmittance of the sky region and the brightness of the image is constructed, the transmittance of the sky and the non-sky region are optimized, and the transmittance map is further improved by using guided filtering. A simulated annealing algorithm is employed to intelligently optimize parameters such as sky segmentation threshold and sky brightness area transmittance, followed by improving the adaptability of the algorithm. Finally, combined with Gaussian filtering and Sobel edge enhancement, the image brightness is further adjusted. Using Information Entropy and NIQE as objective evaluation indexes, combined with subjective evaluation, it is concluded that the proposed method has good convergence and self-adaptive ability, and the objective indexes and subjective effects are better, especially for the hazed images containing air traffic signs.

Improved dark channel priori single image defogging technique using image segmentation and joint filtering.

Foggy images affect image analysis and measurement because of their low definition and blurred details. Despite numerous studies on haze in natural images in hazy environments, the recovery effect is not ideal for processing hazy images in sky areas. A dark channel priori technique for processing haze images with sky areas where atmospheric light values are misestimated and halo artefacts are produced, as well as an improved dark channel priori single-image defogging technique based on image segmentation and joint filtering, are proposed. First, an estimation method of the atmospheric illumination value using image segmentation is proposed to obtain the atmospheric illumination value. The probability density distribution function of the haze-grey image was constructed during image segmentation. The probability density distribution function of the grey image value, the K-means clustering technique, and the method for estimating atmospheric illumination values are combined to improve image segmentation techniques and achieve the segmentation of sky and non-sky areas in hazy images. Based on the segmentation threshold, the number of pixels in the sky and non-sky areas, as well as the normalisation results, were counted to calculate the atmospheric illumination values. Second, to address the halo artefact phenomenon, a method for optimising the image transmittance map using joint filtering is proposed. The image transmittance map was optimised by combining fast-guided filtering and weighted least-squares filtering to retain the edge information and smooth the gradient change of the internal region. Finally, gamma correction and automatic level optimisation are used to improve the brightness and contrast of the defogged images. The experimental results show that the proposed technique can effectively achieve sky segmentation. Compared to the traditional dark-channel prior technique, the proposed technique suppress halo artefacts and improve image detail recovery. Compared to other techniques, the proposed technique exhibited excellent performance in subjective and objective evaluations.

Image Enhancement Algorithm of Foggy Sky with Sky based on Sky Segmentation

In recent years, image defogging has become a research hotspot in the field of digital image processing. Through defogging enhancement processing, the visual quality of foggy images can be significantly improved, and it is also an important part of subsequent image processing. To overcome the limitations of traditional image dehazing, an image enhancement algorithm for foggy image with sky based on sky segmentation is proposed. Firstly, based on K-means clustering and sky feature analysis, sky region recognition is performed for fog image with sky. Secondly, according to the pixels of sky region, the rough transmittance is corrected, and the dehazing image is obtained by dark channel prior image dehazing based on guided filter. Finally, the dehazing image is equalized by bi-histogram equalization. This algorithm effectively avoids the problem of color distortion and halo caused by the traditional dark channel prior dehazing algorithm in the sky area, and makes the restored foggy image have better global and local contrast.

Image dehazing algorithm based on optimized dark channel and haze-line priors of adaptive skysegmentation.

When dealing with outdoor hazy images, traditional image dehazing algorithms are often affected by the sky regions, resulting in appearing color distortions and detail loss in the restored image. Therefore, we proposed an optimized dark channel and haze-line priors method based on adaptive sky segmentation to improve the quality of dehazed images including sky areas. The proposed algorithm segmented the sky region of a hazy image by using the Gaussian fitting curve and prior information of sky color rules to calculate the adaptive threshold. Then, an optimized dark channel prior method was used to obtain the light distribution image of the sky region, and the haze-line prior method was utilized to calculate the transmission of the foreground region. Finally, a minimization function was designed to optimize the transmission, and the dehazed images were restored with the atmospheric scattering model. Experimental results demonstrated that the presented dehazing framework could preserve more details of the sky area as well as restore the color constancy of the image with better visual effects. Compared with other algorithms, the results of the proposed algorithm could achieve higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) evaluation values and provide the restored image with subjective visual effects closer to the real scene.

Adaptive single image defogging based on sky segmentation

Adaptive single image defogging based on sky segmentation

Defogging Algorithm Based on Polarization Characteristics and Atmospheric Transmission Model.

We propose a polarized image defogging algorithm according to the sky segmentation results and transmission map optimization. Firstly, we propose a joint sky segmentation method based on scene polarization information, gradient information and light intensity information. This method can effectively segment the sky region and accurately estimate the global parameters such as atmospheric polarization degree and atmospheric light intensity at infinite distance. Then, the Gaussian filter is used to solve the light intensity map of the target, and the information of the polarization degree of the target is solved. Finally, based on the segmented sky region, a three-step transmission optimization method is proposed, which can effectively suppress the halo effect in the reconstructed image of large area sky region. Experimental results shows that defogging has a big improvement in the average gradient of the image and the grayscale standard deviation. Therefore, the proposed algorithm provides strong defogging and can improve the optical imaging quality in foggy scenes by restoring fog-free images.

Single image dehazing algorithm based on sky segmentation and optimal transmission maps

Single image dehazing algorithm based on sky segmentation and optimal transmission maps

Single image dehazing with sky segmentation and haze density estimation

Single image dehazing with sky segmentation and haze density estimation

Robust Single-Image Dehazing

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 transmissions of bright regions or objects that can generate color distortions during the process of dehazing. In order to suppress the distortions in a large sky area or a bright white object, the sky probabilities and the white-object probabilities calculated in the non-sky area are proposed. The sky area is detected by combining the advantages of a region-based and a boundary-based sky segmentation in order to consider various sky shapes in road scenes. The performance of the proposed methods is evaluated using synthetic and real-world datasets. When compared to conventional methods in the reviewed literature, the proposed method produces significant improvements concerning visual and numerical criteria.

Single image dehazing based on fusion of sky region segmentation

In order to solve the problem of image sharpening with sky region, a single image dehazing method combining sky region segmentation was proposed. Firstly, a fusion sky segmentation method combining OTSU algorithm with Canny operator edge detection is proposed to divide foggy image into sky region and non-sky region. Secondly, a linear index was constructed for the sky region using the dark channel prior sky transmittance to modify the sky region transmittance. For the non-sky region, a fast guided filter was used to optimize the dark channel prior non-sky transmittance. The two were combined to obtain the final transmittance, and the atmospheric light value was optimized by means of average estimation. Finally, combined with the atmospheric scattering model, the image is reconstructed dehaze. The experimental results show that the proposed method can segment the sky more accurately, estimate the atmospheric light value and transmittance more accurately and efficiently, and has the advantages of high contrast, natural color and clear details.

Single Image Dehazing Using Adaptive Sky Segmentation

A new image dehazing algorithm based on adaptive sky region is proposed in this paper, which shows good fidelity in sky region and satisfying visual effect in non‐sky region. For robust sky segmentation, we propose a rough‐to‐fine method that can make a balance between efficiency and accuracy. Considering distribution of haze is inconsistent, we divide the input image into three parts and calculate their atmospheric lights respectively. To solve the problem of invalid dark channel prior, we make an improvement for the transmission estimation. Finally, image fusion is taken as a post processing that can solve the problem of partial darkness and ensure a visual pleasing result. The experimental results for both synthetic and natural hazy images demonstrate that our algorithm performs comparable or even better results than the state‐of‐the‐art methods in terms of various measurement indexes, such as the PSNR, SSIM, and so forth. Besides, the proposed algorithm can be also applied in FPGA platform due to the optimized performance. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Image de-blurring Model based on Machine Learning

Due to the influence of haze in winter, outdoor images usually lose contrast and fidelity. In view of the fact that most de-fog algorithms are not effective for images with large sky areas, an improved dark channel a priori de-fog method is proposed. First of all, the sky region is segmented according to the image gradient information, and on the basis of sky segmentation, the atmospheric light value is reasonably estimated by setting the discriminant formula combined with the high brightness and smoothness of atmospheric light reference pixels. Secondly, according to the different dark channel values, the piecewise linear function is used to dynamically modify the adjustable parameters to solve the local shadow caused by excessive defog. Then, the transmittance estimated by the bright channel model and the improved dark channel prior model are fused, and the edge is optimized by guided filtering. Finally, combined with the atmospheric scattering model, the defog image is obtained by brightness compensation and contrast stretching. The experimental results show that the improved method can effectively improve the image distortion, enhance the image contrast and details, especially in maintaining the visual authenticity of the sky region.

Integrating Machine-Learning-Based Operators in Visual Effects Toolsets

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Post-production workflows rely heavily on image processing and computer vision algorithms for the implementation of their visual effects (VFX) tools. In these fields, machine learning has been shown to be disruptive. By integrating the domain statistics from a given training data set, machine-learning-based operators may be more efficient at solving existing problems and may enable new problems to be solved, expanding the VFX toolset. In this study, we share our experience developing and integrating several machine-learning-based operators into software for the post-production industry. We will present a sky segmentation operator, a depth map estimation operator, and an operator to compute face geometric maps (UVs,<xref ref-type="fn" rid="fn1">^*</xref> depth, and normals) from sequences of images. More specifically, these operators consist of trained deep convolutional neural networks (DCNNs) taking as input an RGB color image and outputting the associated maps, that is, a matte, depth, normals, and/or UV maps. Such maps permit the application of many different effects to the input image during color grading, including beautification or even relighting of faces. These studies will serve as a case study to review and discuss the multiple challenges posed by the implementation, integration, and deployment of machine-learning-based operators into a VFX toolset</i> .

A Robust Method for Dehazing of Single Image with Sky Region Detection and Segmentation

In recent times, there has been a tremendous progress in image dehazing for computer vision applications, while the sky region processed by these algorithms tends to degrade by noise and color distortion. In this paper, an improved dark channel prior algorithm is proposed which detects the sky region first and divides the image into sky region and non-sky region and then estimates the transmission of two parts separately, followed by combining with refining step. The proposed algorithm also accurately corrects the transmission of the sky region to avoid noise and color distortion. Experimental results show a greater quality improvement in the output images than the existing strategies.

Single Haze Image Restoration Under Non-Uniform Dense Scattering Media

We demonstrate an efficient restoration method based on sky segmentation and block optimized transmission (SSBOT) to restore the visibility of single haze image under non-uniform scattering medium. The SSBOT method uses sky region segmentation to determine the haze-opaque region, and estimates the global atmospheric light using quadtree-decomposition. The proposed SSBOT estimates the transmission in every sub-images of the degraded image with dark channel method with coefficient correction. The experimental results show that SSBOT can efficiently restore images degraded by non-uniform dense scattering medium. The proposed method can be used for various security and surveillance applications.

Single image dehazing combining sky segmentation and transmission mapping

Single image dehazing combining sky segmentation and transmission mapping

Sky Segmentation for Enhanced Depth Reconstruction and Bokeh Rendering with Efficient Architectures

We present a high-quality sky segmentation model for depth refinement and investigate residual architecture performance to inform optimally shrinking the network. We describe a model that runs in near real-time on mobile device, present a new, highquality dataset, and detail a unique weighing to trade off false positives and false negatives in binary classifiers. We show how the optimizations improve bokeh rendering by correcting stereo depth misprediction in sky regions. We detail techniques used to preserve edges, reject false positives, and ensure generalization to the diversity of sky scenes. Finally, we present a compact model and compare performance of four popular residual architectures (ShuffleNet, MobileNetV2, Resnet-101, and Resnet-34-like) at constant computational cost.

Inland river image dehazing algorithm based on water surface depth prior

This study proposes a single-image restoration algorithm requiring no additional scene information that is suitable for the removal of haze from images of inland waterways. This algorithm uses a water surface depth of focus prior to obtaining a rough atmospheric-light transmission image, and then applies guided filter refinement and sky segmentation based on grey level histograms to estimate the atmospheric light intensity, thereby performing image dehazing automatically. The performance and applicability of our proposed algorithm are verified by the dehazing results obtained using the proposed algorithm for a large sample set of hazy images of inland waterways compared with those obtained using two standard single-image dehazing algorithms in terms of the processed image quality and processing speed. The results confirm the reliability of the water surface depth of focus prior model. Our method is appropriate for inland waterway images and provides better image quality and computational performance than the existing algorithms.

Performance Assessment of Sky Segmentation Approaches for UAVs

Image segmentation is one of the most challenging research fields for both image analysis and interpretation. The applications of image segmentation could be found as the primary step in various computer vision systems. Therefore, the choice of a reliable and accurate segmentation method represents a non-trivial task. Since the selected image segmentation method influences the overall performance of the remaining system steps, sky segmentation appears as a vital step for Unmanned Aerial Vehicle (UAV) autonomous obstacle avoidance missions. In this paper, we are going to introduce a comprehensive literature survey of the different types of image segmentation methodology followed by a detailed illustration of the general-purpose methods and the state-of-art sky segmentation approaches. In addition, we introduce an improved version of our previously published work for sky segmentation purpose. The performance of the proposed sky segmentation approach is compared with various image segmentation approaches using different parameters and datasets. For performance assessment, we test our approach under different situations and compare its performance with commonly used approaches in terms of several assessment indexes. From the experimental results, the proposed method gives promising results compared with the other image segmentation approaches.

Cloud segmentation property extraction from total sky image repositories using Python

Acquiring the reflectance, radiance, and related structural cloud properties from repositories of historical sky images is a challenging and a computationally intensive task, especially when performed manually or by means of nonautomated approaches. In this article, a quick and efficient, self-adaptive Python tool for the acquisition, and analysis of cloud segmentation properties that is applicable to images from all-sky image repositories is presented and a case study demonstrating its usage and the overall efficacy of the technique is demonstrated. The proposed Python tool aims to build a new data extraction technique and to improve the accessibility of data to future researchers, utilizing the freely available libraries in the Python programing language with the ability to be translated into other programing languages. After development and testing of the Python tool in determining cloud and whole sky segmentation properties, over 42,000 sky images were analyzed in a relatively short time of just under 40 min, with an average execution time of about 0.06 s to complete each image analysis.