Dehazing Technique Research Articles

Efficient single image-based dehazing technique using convolutional neural networks

Efficient single image-based dehazing technique using convolutional neural networks

Research on the dehazing effect of orthogonal polarization method based on atmospheric scattering model and considering extinction ratio parameter

ABSTRACT The hazy weather is currently one of the most common atmospheric environments. It is an urgent problem to realize clear imaging in hazy environment. Orthogonal polarization dehazing is one of the classical dehazing techniques attracting a widespread attention from research workers. However, while using this method, the extinction ratio (ER) parameter of the polarizer was neglected leading to poor dehazing. Therefore, we have introduced the ER of polarizers into the orthogonal polarization dehazing technique in this paper. In the process of sky region division, this study proposes to use HSV, RGB and Lab colour channels to create a comprehensive mask to select the sky region. And the morphological corrosion operation is utilized to increase the region connectivity for better sky region division. Based on subjective visualization, it can be found that the dehazing effect is significantly enhanced with the introduction of ER. The dehazing effect was evaluated based on four parameters of contrast (C 0), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and information entropy (E n ). It is further demonstrated the necessity of introducing ER. The experimental results show that the ER can make the dehazing effect of the orthogonal polarization dehazing technique improved, which is valuable for the engineering application of image processing in hazy environment. The experimental results proved to be encouraging.

Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning

Traditional dehazing approaches that rely on prior knowledge exhibit limited efficacy when confronted with the intricacies of real-world hazy environments. While learning-based dehazing techniques necessitate large-scale datasets for effective model training, the acquisition of these datasets is time-consuming and laborious, and the resulting models may encounter a domain shift when processing real-world hazy images. To overcome the limitations of prior-based and learning-based dehazing methods, we propose a self-supervised remote sensing (RS) image-dehazing network based on zero-shot learning, where the self-supervised process avoids dense dataset requirements and the learning-based structures refine the artifacts in extracted image priors caused by complex real-world environments. The proposed method has three stages. The first stage involves pre-processing the input hazy image by utilizing a prior-based dehazing module; in this study, we employed the widely recognized dark channel prior (DCP) to obtain atmospheric light, a transmission map, and the preliminary dehazed image. In the second stage, we devised two convolutional neural networks, known as RefineNets, dedicated to enhancing the transmission map and the initial dehazed image. In the final stage, we generated a hazy image using the atmospheric light, the refined transmission map, and the refined dehazed image by following the haze imaging model. The meticulously crafted loss function encourages cycle-consistency between the regenerated hazy image and the input hazy image, thereby facilitating a self-supervised dehazing model. During the inference phase, the model undergoes training in a zero-shot manner to yield the haze-free image. These thorough experiments validate the substantial improvement of our method over the prior-based dehazing module and the zero-shot training efficiency. Furthermore, assessments conducted on both uniform and non-uniform RS hazy images demonstrate the superiority of our proposed dehazing technique.

A Fast-Dehazing Technique using Generative Adversarial Network model for Illumination Adjustment in Hazy Videos

Haze significantly lowers the quality of the photos and videos that are taken. This might potentially be dangerous in addition to having an impact on the monitoring equipment' dependability. Recent years have seen an increase in issues brought on by foggy settings, necessitating the development of real-time dehazing techniques. Intelligent vision systems, such as surveillance and monitoring systems, rely fundamentally on the characteristics of the input pictures having a significant impact on the accuracy of the object detection. This paper presents a fast video dehazing technique using Generative Adversarial Network (GAN) model. The haze in the input video is estimated using depth in the scene extracted using a pre trained monocular depth ResNet model. Based on the amount of haze, an appropriate model is selected which is trained for specific haze conditions. The novelty of the proposed work is that the generator model is kept simple to get faster results in real-time. The discriminator is kept complex to make the generator more efficient. The traditional loss function is replaced with Visual Geometry Group (VGG) feature loss for better dehazing. The proposed model produced better results when compared to existing models. The Peak Signal to Noise Ratio (PSNR) obtained for most of the frames is above 32. The execution time is less than 60 milli seconds which makes the proposed model suited for video dehazing.

Improving Semantic Segmentation Under Hazy Weather for Autonomous Vehicles Using Explainable Artificial Intelligence and Adaptive Dehazing Approach

Haze-level discriminators are highly desirable for autonomous vehicles to handle segmentation tasks successfully in hazy and foggy outdoor environments. Neural networks trained to detect clear images show more false positives and unrecognize the pixel patterns for the class categories when they encounter hazy images. Here, we propose a novel dehazing scheme called Adaptive Dehazing (AD) which can separate the unacceptable hazy images and apply the dehazing technique only to those images before passing it to the neural network for segmentation. Various thresholds have been defined to classify hazy images into four hazy categories: heavy, moderate, slight and clear. Also, a hazy image generator is implemented to create synthetic hazy images that replicate the actual hazy road scene conditions for testing the proposed algorithm. In addition, we use the Explainable Artificial Intelligence (XAI) method to understand the feature selected by different layers in the network before and after applying the AD and their contribution to improving the Intersection over Union (IoU) and pixel accuracy (PA) of the segmented categories. Extensive experiments demonstrate quantitatively as well as qualitatively the performance improvement in the segmentation task. Our AD scheme significantly outperforms the previous state-of-the-art dehazing methods.

Image Dehazing Technique Based on Sky Weight Detection and Fusion Transmission

<p>Computer vision techniques are widely applied to the object detection, license plate recognition, remote sensing, and outdoor monitoring system. The performance of these applications mainly relies on the high quality of outdoor image. However, an outdoor image can be led to contrast decrease, color distortion, and unclear structure by poor weather conditions and human factors such as haze, fog, and air pollution. These issues may lower down the sharpness of a photo. Despite of the single-image dehazing is used to solve these issues, it cannot achieve a satisfactory result when the method deals with the bright scene and sky area. In this article, we aim to design an adaptive dehazing technique based on fusion transmission and sky weight detection. The sky weight detection is employed to distinguish the foreground and background, while detected results are applied to the fusion strategy to calculate deep and shallow transmissions. Thus, this can get rids of the subject of over-adjustment. Experimental results have demonstrated that the new method can outperform the latest state-of-the-art methods in terms of subjective and the objective assessments.</p> <p> </p>

CNN 영상 회귀 기반의 산란계수 추정을 통한 연무제거

The estimation of the scattering coefficient in depth image-based dehazing is of paramount importance. Since scattering coefficients are used to estimate the transmission image for dehazing, the optimal scattering coefficients for effective dehazing must be obtained depending on the level of haze and fog generation. In this study, we performed a CNN-based image regression to obtain the optimal scattering coefficients for each image with fog and haze. A three-channel image was used as the input data, and the learning was performed with approximately 2,000 labeled synthetic haze and fog datasets. Subsequently, the transmission image was estimated using the scattering coefficient obtained for the input image through the learned model, and the depth image was obtained through the LiDAR point cloud projection for performing the dehazing. This paper presents a qualitative and quantitative comparison of the results obtained using the proposed dehazing technique with those obtained using the existing dehazing algorithms.

Haze Removal Based on Refined Transmission Map for Aerial Image Matching

A novel strategy is proposed to address block artifacts in a conventional dark channel prior (DCP). The DCP was used to estimate the transmission map based on patch-based processing, which also results in image blurring. To enhance a degraded image, the proposed single-image dehazing technique restores a blurred image with a refined DCP based on a hidden Markov random field. Therefore, the proposed algorithm estimates a refined transmission map that can reduce the block artifacts and improve the image clarity without explicit guided filters. Experiments were performed on the remote-sensing images. The results confirm that the proposed algorithm is superior to the conventional approaches to image haze removal. Moreover, the proposed algorithm is suitable for image matching based on local feature extraction.

A joint cumulative distribution function and gradient fusion based method for dehazing of long shot hazy images

Hazy or foggy weather conditions significantly degrade the visual quality of an image in an outdoor environment. It also changes the color and reduces the contrast of an image. This paper introduces a novel single image dehazing technique to restore a hazy image without considering the physical model of haze formation. In order to find haze-free image, the proposed method does not require the transmission map and its costly refinement process. Since haze effect is dependent on the depth, it severely degrades the visibility of the objects located at a far distance. The objects close to the camera are unaffected. In this paper, we propose a fusion-based haze removal method based on the joint cumulative distribution function (JCDF) that treats faraway haze and nearby haze separately. The output images after the JCDF module, fused in the gradient domain to produce a haze-free image. The proposed method not only significantly enhances visibility but also preserves texture details. The proposed method is experimented and evaluated on a large set of challenging hazy images (large scene depth, night time, dense fog, etc.). Both qualitative and quantitative measures show that the performance of the proposed method is better than the state-of-the-art dehazing techniques.

Design and Implementation of Aerial Vehicle Remote Sensing and Surveillance System, Dehazing Technique Using Modified Dark Channel Prior

Design and Implementation of Aerial Vehicle Remote Sensing and Surveillance System, Dehazing Technique Using Modified Dark Channel Prior

Color Cast Dependent Image Dehazing via Adaptive Airlight Refinement and Non-Linear Color Balancing

Hazy images suffer from low visibility since the light gets scattered as it passes through various atmospheric particles. Moreover, such images are prone to color distortion, particularly in real weather conditions like sandstorms. In this letter, an effective dehazing technique is proposed using weighted least squares filtering on dark channel prior and color correction that involves automatic detection of color cast images. We show that the spread of the hue in a hazy image can differentiate a color cast image from a non-cast one. We propose a measure using the same for categorizing hazy images as cast and non-cast ones. Our novel color correction is performed by color balancing using a non-linear transformation followed by a cast-adaptive airlight refinement. Subjective and quantitative evaluations show that our method outperforms the state-of-the-art. It removes cast satisfactorily and reduces haze substantially while maintaining the naturalness of the image. Moreover, it produces visually pleasing images without halo artifacts.

A Novel Dehazing Technique for Underwater Image Using Nakagami Distribution

Due to light absorption and scattering most of the underwater images suffers from poor visibility, lack of contrast and color casting, foggy appearance etc. As a result, important information from the image is also lost. There are many approaches to enhance the underwater image quality using different methodologies. The aim of this paper is restoring the color, contrast and lost information from underwater images with the help a new methodology using contrast stretching. The stretching process is applied to the images using Nakagami distribution. The proposed methodology has been tested on about 100 underwater images. The qualitative and quantitative comparison with Histogram Equalization Method (HE), Integrated Color Model(ICM) and Unsupervised Color Correction Method (UCM) has been done. In most of the cases we have found our result is better than the others.

Dehazing Effects on Image and Videousing AHE, CLAHE and Dark Channel Prior

The image captured by camera is degraded by various atmospheric parameters for example rain, storm, wind, haze, snow. The removing haze is called dehazing, is naturally done in the physical degradation model that requires a resolution of an ill-posed inverse problem. In this paper discussion and e relative study of Adaptive Histogram Equalization (AHE) as well as Contrast limited adaptive histogram equalization (CLAHE) and dark channel prior (DCP). This article suggest image and video dehazing technique working on DCP method. The DCP is resulted from the characteristics of images taken in outdoor that the value of intensity inside the local window is nearly equal to zero. The DCP system has good haze elimination and color managing potential for the images with various angles of haze. The dehazing is done using following four major steps: atmospheric light estimation, transmission map estimation, transmission map refinement, and image reconstruction. This solution of four step DCP will give solution to ill-posed inverse problem. This dehazing techniques can be used in advanced driverless assisted systems in autonomous cars, satellite imaging, underwater imaging etc.

Fast and memory efficient de-hazing technique for real-time computer vision applications

Some features of an image may spoil due to fog or haze, smoke. These images lose their brightness due to air-light scattering. It offers troublesomeness to the people lives in hill and fog regions of the world. This paper proposed two key aspects. One is a modified dark-channel method based on the median for eliminating the refine the transmission map as well as halos and artifacts, another important aspect is a memory-efficient row-based arrangement of the pixels for real-time applications. The advantage of this method is air-light can be predicted directly from the modified dark channel and also accurate transmission map can be estimated. This method is compared with other existing four algorithms. Our proposed method analyzed in terms of Peak Signal to Noise Ratio (PSNR), Average Time cost (ATC), percentage of haze improvement (PHI), average contrast of output image (ACOI), Mean Squared Error (MSE) and Structural Similarity Index (SSIM). The quality of the output de-haze image of our algorithm over existing algorithms is more. It has taken less computation time, equal MSE, with higher SSIM and has more percentage of haze improved over existing methods.

An Effective Screening System to Detect Diabetic Retinopathy by using Dehazing Technique

The Diabetic Retinopathy (DR) is playing a crucial role in clinical data analysis to diagnose abnormality in retina. Many situations the early stage of patient is not aware of any symptoms until it is too late for effective treatment. The abnormality in the blood vessels of diabetics, a way will be paved for prompt diagnosis of DR. In this work, we proposed the Dehazing method of fundus image to detect and classify the disease condition based on changes in blood vessels using thresholding segmentation technique using mean square error (MSE). Then formulate the area of extracted blood vessels in the subsequent analysis to classify accurately.

Image dehazing using window-based integrated means filter

Image acquisition is generally susceptible to poor environmental conditions such as fog, smog, haze, etc. However, designing an efficient image dehazing technique is still an ill posed problem. Extensive review of the competitive haze removal approaches reveal that the texture preservation and computational speed are still a challenging issues. Therefore, in this paper, initially, a mask is utilized to decompose an input image into low and high frequency regions based on image gradient magnitude. Thereafter, a Gradient sensitive loss (GSL) is designed to obtain the depth information from an input hazy image. Thereafter, transmission map is refined by designing an efficient filter named as Window-based integrated means filter (WIMF). Finally, the restoration model is utilized to recover the hazy images. Experimental analysis reveals that the proposed dehazing technique achieves considerable results beyond the prototypes of the benchmarks. Additionally, the proposed technique outperforms the state-of-the-arts in single image dehazing approaches.

Image Dehazing Technique Based On DWT Decomposition and Intensity Retinex Algorithm

Conventional designs use multiple image or single image to deal with haze removal. The presented paper uses median filer with modified co-efficient (16 adjacent pixel median) and estimate the transmission map and remove haze from a single input image. The median filter prior(co-efficient) is developed based on the idea that the outdoor visibility of images taken under hazy weather conditions seriously reduced when the distance increases. The thickness of the haze can be estimated effectively and a haze-free image can be recovered by adopting the median filter prior and the new haze imaging model. Our method is stable to image local regions containing objects in different depths. Our experiments showed that the proposed method achieved better results than several state-of-the-art methods, and it can be implemented very quickly. Our method due to its fast speed and the good visual effect is suitable for real-time applications. This work confirms that estimating the transmission map using the distance information instead the color information is a crucial point in image enhancement and especially single image haze removal.

A novel dehazing model for remote sensing images

The visibility of remote sensing images taken in poor environmental conditions is often degraded due to the occurrence of fog, haze, mist, and smog. To restore these images, many visibility restoration techniques have been developed. However, the existing techniques perform poorly to remove the effect of haze from remote sensing images with a complex background and large haze gradient. In this paper, a gradient profile prior is proposed to remove the haze from remote sensing images. The coarse estimated atmospheric veil has been refined by using gradient-based guided image filter. Moreover, the visibility restoration model has also been modified to overcome the over saturation and color distortion problems. Extensive experiments have shown that the proposed dehazing technique outperforms the existing image dehazing techniques. The proposed technique has significantly improved the spatial and radiometric information of remote sensing images.

Efficient Dehazing Technique for Hazy Images using DCP and WAF

Efficient Dehazing Technique for Hazy Images using DCP and WAF

Dehazing of outdoor images using notch based integral guided filter

The dehazing problem is an ill-posed and can be regularized by designing an efficient filter to refine the coarse estimated atmospheric veil. The most of existing dehazing techniques suffer from over-saturation, halo artifacts, and gradient reversal artifacts problems. In this paper, a dehazing technique is proposed to remove halo and gradient reversal artifacts problem. In this technique, a notch based integral guided filter is proposed. Moreover, the visibility restoration model is also modified to reduce over-saturation problem. The proposed dehazing technique is compared with seven well-known existing dehazing techniques over ten benchmark hazy images. The experimental results demonstrate that proposed technique is able to remove the haze from hazy images as well as significantly improve the image’s visibility. It also reveals that the restored image has little or no artifacts.