Non-sky Regions Research Articles

Single Image Dehazing via Regional Saturation-Value Translation

This paper proposes an image dehazing prior, called Regional Saturation-Value Translation (RSVT), in order to address the color distortion issues produced by prevailing prior-based dehazing methods when processing hazy images with large sky regions. The proposed RSVT prior is derived from statistical analyses of the correlation between hazy points and respective haze-free points in the HSV color space. The prior is based upon two key observations in the sky areas. First, the difference in terms of hue for a pair of hazy and haze-free points is very small, raising an assumption that the variability of pixel values caused by haze mostly occurs in the saturation and value spaces. This leads to the second observation that, in the 2D saturation-value coordinate system, almost all the lines passing through corresponding pairs of hazy-clean points, termed S-V lines, are likely to intersect around the airlight coordinates. A hybrid refined dark channel is introduced in order to decompose the input hazy image into sky and non-sky regions and to estimate the global atmospheric light. Combining the prior with the hybrid refined dark channel, a novel single image dehazing framework is proposed. Haze removal is performed separately for the sky and non-sky regions by adopting the proposed RSVT prior and Koschmieder's law, respectively. The experimental results have shown that the proposed dehazing method can restore visually compelling sky color and effectively handle the color distortion issues associated with large sky regions.

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.

Sky-GVINS: a sky-segmentation aided GNSS-Visual-Inertial system for robust navigation in urban canyons

ABSTRACT Integrating Global Navigation Satellite Systems (GNSS) in Simultaneous Localization and Mapping (SLAM) systems draws increasing attention to a global and continuous localization solution. Nonetheless, in dense urban environments, GNSS-based SLAM systems will suffer from the Non-Line-Of-Sight (NLOS) measurements, which might lead to a sharp deterioration in localization results. In this paper, we propose to detect the sky area from the up-looking camera to improve GNSS measurement reliability for more accurate position estimation. We present Sky-GVINS: a sky-aware GNSS-Visual-Inertial system based on a recent work called GVINS. Specifically, we adopt a global threshold method to segment the sky regions and non-sky regions in the fish-eye sky-pointing image and then project satellites to the image using the geometric relationship between satellites and the camera. After that, we reject satellites in non-sky regions to eliminate NLOS signals. We investigated various segmentation algorithms for sky detection and found that the Otsu algorithm reported the highest classification rate and computational efficiency, despite the algorithm’s simplicity and ease of implementation. To evaluate the effectiveness of Sky-GVINS, we built a ground robot and conducted extensive real-world experiments on campus. Experimental results show that our method improves localization accuracy in both open areas and dense urban environments compared to the baseline method. Finally, we also conduct a detailed analysis and point out possible further directions for future research. For detailed information, visit our project website at https://github.com/SJTU-ViSYS/Sky-GVINS.

Single-Image Dehazing Based on Improved Bright Channel Prior and Dark Channel Prior

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 address a large band of haze, we propose a simple yet effective method for single-image dehazing based on an improved bright prior and dark channel prior. First, we use the Otsu method by particle swarm optimization to divide the hazy image into sky regions and non-sky regions. Then, we use the improved bright channel prior and dark channel prior to estimate the parameters in the physical model. Second, we propose a weighted fusion function to efficiently fuse the parameters estimated by two priors. Finally, the clear image is restored through the physical model. Experiments illustrate that our method can solve the problem of the invalidation of the dark channel prior in the sky region well and achieve high-quality image restoration, especially for images with limited haze.

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.

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.

Image Defogging Framework Using Segmentation and the Dark Channel Prior.

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 very promising defogging technique due to excellent restoring results for images containing no homogeneous region. However, having a large homogeneous region such as sky region, the restored images suffer from color distortion and block effects. Thus, to overcome the limitation of DCP method, we introduce a framework which is based on sky and non-sky region segmentation and restoring sky and non-sky parts separately. Here, isolation of the sky and non-sky part is done by using a binary mask formulated by floodfill algorithm. The foggy sky part is restored by using Contrast Limited Adaptive Histogram Equalization (CLAHE) and non-sky part by modified DCP. The restored parts are blended together for the resultant image. The proposed method is evaluated using both synthetic and real world foggy images against state of the art techniques. The experimental result shows that our proposed method provides better entropy value than other stated techniques along with have better natural visual effects while consuming much lower processing time.

Image Dehazing Using Near-Infrared Information Based on Dark Channel Prior

Images collected in haze or other weather have problems of quality degradation, blurred details, and low recognition, which seriously affect the application and development of related image application fields. The dark channel prior method is the typical method of image dehazing, but it is not suitable for sky region. The wavelength of near-infrared is longer than that of visible light, which makes it more penetrating, less affected by the scattering of suspended particles in the air, and carries more detailed information. This paper uses near-infrared information to distinguish between sky region and non-sky region, improve the dark channel prior method, enhance the visibility of the image, and restore the contrast of the image. A large number of experiments have proved that our method has achieved satisfactory results in solving problems such as sky region and darker scene. This method has strong pertinence and stability for image dehazing, and the effect is relatively natural and efficient. Therefore, our method is better than the existing method of image dehazing.

Enhancement of Hazy Images Using Atmospheric Light Estimation Technique

Images captured in degraded weather conditions often suffer from bad visibility. Pre-existing haze removal methods, the ones that are effective are computationally complex too. In common de-hazing approaches, estimation of atmospheric light is not achieved properly as a consequence, haze is not removed significantly from the sky region. In this paper, an efficient method of haze removal from a single image is introduced. To restore haze-free images comprising of both sky as well as nonsky regions, we developed a linear model to predict atmospheric light and estimated the transmission map using the dark channel prior followed by an application of a guided filter for quick refinement. Several experiments were conducted on a large variety of images, both reference and nonreference, where the proposed image de-hazing algorithm outperforms most of the prevalent algorithms in terms of perceptual visibility of the scene and computational efficiency. The proposed method has been empirically measured through quantitative and qualitative evaluations while retaining structure, edges, and improved color.

Segmentation-based image defogging using modified dark channel prior

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.

The enhancement of catenary image with low visibility based on multi-feature fusion network in railway industry

In the Industrial Internet of Things (IIoT), the security and efficiency are indispensable. For the railway industry, the video from inspection vehicle would be influenced by various factors with low visibility and hard for high level vision task, such as the fault diagnosis of catenary system. In this paper, we propose a method based on the multi-feature fusion network to improve the quality and visual effect of the catenary images. The transmission map is learned from the multi-scale and multi-feature fusion network, which would learn coarse and fine details and combine the latent features. In the catenary image, the sky and non-sky regions are segmented through multiple accommodative thresholds to estimate the atmospheric light value. With the refinement of transmission map, the restored catenary image is obtained through the atmospheric scattering model. In the experimental results, it can be seen that the proposed method can improve the clarity of catenary image in haze. The quantitative evaluation shows that it has better visual effect compared with the other traditional methods.

Joint Raindrop and Haze Removal from a Single Image.

In a recent study, it was shown that, with adversarial training of an attentive generative network, it is possible to convert a raindrop degraded image into a relatively clean one. However, in real world, raindrop appearance is not only formed by individual raindrops, but also by the distant raindrops accumulation and the atmospheric veiling, namely haze. Current methods are limited in extracting accurate features from a raindrop degraded image with background scene, the blurred raindrop regions, and the haze. In this paper, we propose a new model for an image corrupted by the raindrops and the haze, and introduce an integrated multi-task algorithm to address the joint raindrop and haze removal (JRHR) problem by combining an improved estimate of the atmospheric light, a modified transmission map, a generative adversarial network (GAN) and an optimized visual attention network. The proposed algorithm can extract more accurate features for both sky and non-sky regions. Experimental evaluation has been conducted to show that the proposed algorithm significantly outperforms state-of-the-art algorithms on both synthetic and real-world images in terms of both qualitative and quantitative measures.

Single image dehazing via a dual-fusion method

Single image dehazing is a challenging task because of the hue and brightness distortion problems. In this paper, we propose a dual-fusion method for single image dehazing. By a segmentation method creating two divided regions, the sky and non-sky regions can be obtained. To properly optimize the transmission, a multi-region fusion method is proposed for single image smooth. An exposure fusion method is constructed by the brightness transform function to effectively remove the haze from a single image. Experimental results show that this method outperforms state-of-the-art dehazing methods in terms of both efficiency and the dehazing effect.

Improved Single Image De-Hazing Via Sky Region Detection, Classification and Illumination Refinement

This paper presents an automated sky detection technique based on statistical and fuzzy rule-based edge detection for improved hazy image contrast enhancement. This is significant since most conventional de-hazing approaches yield hazy images with over-enhanced sky regions and under-enhanced detail regions due to inability to adaptively determine and enhance such regions. Earlier and current schemes developed to remedy this issue are highly complex, usually require training with vast amount of images and manual tuning of one or several parameters. The proposed method utilizes standard deviation and fuzzy logic-based edge detection combined with thresholding algorithms to generate a homogeneity map identifying sky and non-sky regions. The areas of these regions are subsequently computed and used to obtain a homogeneity ratio. The ratio is then used to trigger a decision-based, switching scheme incorporated into a partial differential equation (PDE) de-hazing algorithm to improve results. Alternatively, a log illumination refinement method is proposed as a less complex alternative combined with the modified PDE algorithm to process hazy images without degrading sky regions, while yielding brighter images. Several image datasets from the literature were used to validate the proposed approaches and yielded mostly consistent and comparable results similar to or better than algorithms from the literature.

Dark Channel: The Devil is in the Details

Dark channel prior is a highly valued theory mostly used in the area of haze removal. It reveals the wide existence of pixels with very small channel. However, the definition is in a decorated way that relies on the concept of dark channel. The dark channel works like an image domain wherein non-sky regions of clear outdoor images are close to zero. In this letter we show that, such a concept is unnecessary and misleading, and can be improved by using a more basic concept named dark pixel. A novel transmission estimation method based on finding dark pixels is proposed. The method is concise and easy to understand. In comparisons with state-of-the-art methods, our method shows significant improvements on both transmission estimation and haze removal results.

Haze removal method based on a variation function and colour attenuation prior for UAV remote-sensing images

ABSTRACTAiming at the problem of low visibility of images obtained by UAV in hazy weather, this paper proposes an image dehazing algorithm based on variation function and colour attention prior. A large number of experiments have proved that the sky or other bright regions could affect the estimation of atmospheric light and transmittance. In the experiment, our proposed algorithm divides the images into sky and dark regions and uses the pixels of the dark region to solve the atmospheric light value. According to the region where the pixels are located, the transmittances of the pixels in the sky and non-sky regions are separately estimated and adjusted. The experiment’s results show that the restored image visibility, information entropy and colour saturation are significantly improved, and the algorithm’s computational efficiency is high.

An image dehazing algorithm based on double priors constraint

Aiming at the color distortion of the restored image in the sky region, we propose an image dehazing algorithm based on double priors constraint. Firstly, we divided the haze image into sky and non-sky regions. Then the Color-lines prior and dark channel prior are used for estimating the transmission of sky and non-sky regions respectively. After introducing color-lines prior to correct sky regions restored by the dark channel prior, we get an accurate transmission. Finally, the local media mean value and standard deviation are used to refine the transmission to obtain the dehazing image. Experimental results show that the algorithm has obvious advantages in the recovery of the sky area.

An Efficient Sky Detection Algorithm From Fisheye Image Based on region classification and segment analysis

In this paper, an efficient approach for automatic and accurate sky region detection from fisheye images is proposed. The proposed approach starts by segmenting the acquired image into regions using Statistical Region Merging method. After that, the segmented regions are characterized using local RGB color descriptor using image quantization. The next step consists of classifying the characterized regions into sky and non-sky regions by using maximal similarity based region classification through Hellinger kernel-based distance. In order to improve the obtained region classification results, a segment analysis based technique using Line Segment Detector is proposed. Experimental results prove the robustness and performance of the proposed procedure.

Single image fog and haze removal based on self-adaptive guided image filter and color channel information of sky region

In this paper, we report an effective algorithm for removing both fog and haze from a single image. Existing algorithms based on atmospheric degeneration model generally lead to non-definite solutions for the haze and thick fog images, though they are very efficient for thin fog images. In general, as the algorithms based on vision enhancement cannot automatically adjust weight coefficient for the different structure images, the excessive or inadequate enhancement may emerge. In this paper an original degradation image is primarily segmented into the sky and non-sky regions, and then the main boundaries of non-sky region are extracted using L0 smoothing filter. So our vision enhancement algorithm automatically adjusts weight coefficient according to various structure images. At the stage of vision enhancement, guided image filter famous for its excellent boundary preservation is adopted. As for haze image, the color channel information scattered by haze particles can be obtained in the sky region to make an effective color correction. Both the subjective and objective evaluations of experimental results demonstrate that the proposed algorithm has more outstanding recovery effect for haze and thick fog images. Moreover, the proposed algorithm can judge fog or haze image, which is a by-product of this research.

Dehazing for images with large sky region

This paper introduces a haze removal algorithm based on region decomposition and features fusion to overcome the challenges of the dark channel prior-based algorithm, such as block effect and color distortion. In our proposed method, an image is decomposed with the quad-tree method based on gradient and grayscale information to obtain the sky regions. These sky regions are used as the seed point for region-growing, which will segment the image into sky and non-sky regions. A Gaussian filter is applied for smoothing on the segmented image, which is then used as a weight map to optimize the transmission image in the dark channel prior algorithm. Finally, the haze-free images are obtained based on an atmospheric scattering model and color compensation. Our experimental results demonstrated that images restored using this algorithm are generally clear and natural, and the algorithm is especially suitable for hazy images with large sky regions.