Dark Channel Prior Research Articles

White Channel Prior: 개선된 Dark Channel Prior를 이용한 안개 제거 연구

This paper proposes a new dehaze method to improve the contrast value and color intensity degradation of the result image that occurs when the haze is removed through Dark Channel Prior, a representative algorithm of the prior based dehaze method. Due to the estimate and restores by defined a dark channel based on the minimum values of the R, G, and B channels of the local patch, the existing dehaze method decrease the reconstruction image in contrast and color intensity. Therefore, to improve this, this paper proposes White Channel Prior that improve the reduced contrast and color intensity by reprocessing the image processed by the Dark Channel Prior algorithm in the opposite concept to DCP. The proposed method improves the image quality by reconstructing the contrast and color intensity degradation problem of the existing method. As a result of a comparison experiment between the proposed method and the conventional method, the dehaze image to which White Channel Prior method is applied shows a subjective quality improvement compared to the existing method results.

Remote sensing image uneven haze removal based on correction of saturation map

Remote sensing images are frequently affected by the random haze. An efficient method to remove uneven haze is proposed based on dark channel prior (DCP) corrected by saturation map and atmospheric haze model with consideration of multiple atmospheric scattering effects. First, the non-negligible atmospheric multiple scattering phenomenon is simulated by atmosphere point spread function, and the atmospheric multiple scattering haze model is established. Second, an effect method is proposed to calculate the saturation map to correct the DCP, which is used to eliminate the intensity residual of remote sensing images and re-establish the transmission calculation formula. Finally, the image is entered into the integrated restoration framework to remove the haze. Due to the correction of the saturation map, this method can effectively remove the inhomogeneous haze and retain detailed information in the haze-free areas. Compared with other excellent dehaze approaches, qualitative and quantitative experiments are carried out to demonstrate that the proposed method can effectively recover scenes in hazy regions and can retain more detailed information in haze-free regions.

Detail Preserving Low Illumination Image and Video Enhancement Algorithm Based on Dark Channel Prior.

In low illumination situations, insufficient light in the monitoring device results in poor visibility of effective information, which cannot meet practical applications. To overcome the above problems, a detail preserving low illumination video image enhancement algorithm based on dark channel prior is proposed in this paper. First, a dark channel refinement method is proposed, which is defined by imposing a structure prior to the initial dark channel to improve the image brightness. Second, an anisotropic guided filter (AnisGF) is used to refine the transmission, which preserves the edges of the image. Finally, a detail enhancement algorithm is proposed to avoid the problem of insufficient detail in the initial enhancement image. To avoid video flicker, the next video frames are enhanced based on the brightness of the first enhanced frame. Qualitative and quantitative analysis shows that the proposed algorithm is superior to the contrast algorithm, in which the proposed algorithm ranks first in average gradient, edge intensity, contrast, and patch-based contrast quality index. It can be effectively applied to the enhancement of surveillance video images and for wider computer vision applications.

Single image dehazing based on pixel-wise transmission estimation with estimated radiance patches

Images acquired outdoors can be affected by atmospheric conditions, such as fog and haze, and image dehazing is used to restore scene radiance in hazy images. In image dehazing, atmospheric light and transmission estimation are essential; transmission estimation is an essential step. In particular, the dark channel prior (DCP) is widely used for the transmission estimation. When using DCP-based methods, an initial transmission map is obtained through a morphological operation based on the assumption that the scene transmission in a local area is constant. However, the depth discontinuity problem cannot be avoided, and outliers are produced in the process of refining the initial transmission map. In addition, the estimation accuracy varies depending on the scene configuration due to the limitations of DCP-based methods, which simply estimate the transmission based on pixel intensity. To overcome these problems, we propose the pixel-wise transmission estimation method with estimated radiance patches (PTERP) for image dehazing. We first approximate the transmission range in the pixel location using the transmission map obtained using DCP. A patch is then set around each pixel, and several estimated radiance patches are obtained using each value belonging to the transmission range. The transmission value in the corresponding pixel location is determined using the information from the estimated radiance patches. The transmission map is then obtained by estimating the transmission value for each pixel in the entire image. With this approach, scene radiance can be restored using the determined transmission map. We performed experiments using various images, and the results demonstrated that proposed PTERP outperformed the conventional methods both quantitatively and qualitatively.

Efficient Sandstorm Image Enhancement Using the Normalized Eigenvalue and Adaptive Dark Channel Prior

A sandstorm image has features similar to those of a hazy image with regard to the obtaining process. However, the difference between a sand dust image and a hazy image is the color channel balance. In general, a hazy image has no color cast and has a balanced color channel with fog and dust. However, a sand dust image has a yellowish or reddish color cast due to sand particles, which cause the color channels to degrade. When the sand dust image is enhanced without color channel compensation, the improved image also has a new color cast. Therefore, to enhance the sandstorm image naturally without a color cast, the color channel compensation step is needed. Thus, to balance the degraded color channel, this paper proposes the color balance method using each color channel’s eigenvalue. The eigenvalue reflects the image’s features. The degraded image and the undegraded image have different eigenvalues on each color channel. Therefore, if using the eigenvalue of each color channel, the degraded image can be improved naturally and balanced. Due to the color-balanced image having the same features as the hazy image, this work, to improve the hazy image, uses dehazing methods such as the dark channel prior (DCP) method. However, because the ordinary DCP method has weak points, this work proposes a compensated dark channel prior and names it the adaptive DCP (ADCP) method. The proposed method is objectively and subjectively superior to existing methods when applied to various images.

An improved dark channel defogging algorithm based on the HSI colour space

When the traditional dark channel prior (DCP) algorithm is used for image defogging, it faces some problems such as unsatisfactory restoration of the target edge details, distortion in large-scale and high-brightness sky regions. To solve these problems, this paper proposes an improved dark channel defogging algorithm based on the HSI colour space, named the IDCP algorithm. First, an asymmetric mean filter window is used to obtain the modified dark channel image to improve the grey value mutation in the traditional dark channel image. Based on this work, an improved global atmospheric transmittance calculation method is proposed, which effectively improves the inapplicability of the dark channel prior in large-scale and high-brightness sky regions. Finally, the Laplacian operator is used to sharpen the image to enhance the target edge information. Simulation results show that the proposed algorithm can effectively solve the distortion phenomenon caused by the traditional DCP algorithm when handling large-scale and high-brightness sky regions, improve the target edge details, and restore the true colour of the image scene.

Underwater video transmission with video enhancement using reduce hazing algorithm

Abstract Underwater video regulation is an insightful research field that can help engineers with bettering investigation on the lowered condition. Submerged video preparing has been utilized in a many fields, such as submerged infinitesimal location, landscape examining, mine identification, media transmission connections, and self-proficient lowered vehicles. Be that as it may, submerged video experiences solid assimilation, dissipating, shading contortion, and clamor from the manufactured light sources, causing video obscure, cloudiness, and a somewhat blue or greenish tone. In this way, the improvement can be separated into two techniques, submerged video de-preliminaries and underexposed video concealing remaking. Relentless in remote correspondence structures, for instance 3G, 4G, and so on, a coming crisis is endless deftly of the nonattendance of consistently Radio Frequency (RF) resources; this deterrent in moving speed cannot strengthen the improvement notable for high information speed. So the new innovation of Light-Fidelity (Li-Fi) came into picture. This innovation can be contrasted to that of Wi-Fi and offers points of interest like expanded available spectrum efficiency, effectiveness, security, low idleness and a lot higher speed. Communication is accomplished by exchanging light-emitting diode (LED) lights on and off at a speed higher than what is detectable to the human eye. This paper presents the explanation behind underexposed picture corruption and surveys the cutting-edge knowledge calculations like video reduce hazing algorithm. In this calculation, it uses two different de-hazing methods, simple Dark Channel Prior (DCP) and Approximate Dark Channel Prior (ADCP), to reduce haze in a video.

Intelligent driving system at opencast mines during foggy weather

ABSTRACT The fog in mining operations minimises the visibility, preventing drivers from a clear view, causing accidents and vehicle collisions. This paper provides an intelligent driving system for heavy earthmoving machinery operators in opencast mines, including hardware and software. Hardware contains high definition and thermal cameras, a global navigation satellite system (GNSS), radar, laser light, wireless devices, graphical processing unit, touch screen, etc. The software covers image stitching, image enhancement, and convolution neural network-based object detection. The display dashboard is divided into four windows. Each window represents a different view, i.e. 180° panorama view of the driving lane, GNSS tracking map, proximity radar detection view, and rear thermal camera view. An additional colour transfer method has been used in the existing image stitching method to reduce misalignment and ghost effect in the panorama output. The proposed method outperformed the existing methods, namely contrast limited adaptive histogram equalisation (CLAHE) and dark channel prior (DCP). The proposed image enhancement technique has increased contrast, entropy, and colour average by 0.069, 0.43, and 13.96, respectively, than CLAHE, and 0.994, 0.43, and 42.07 than DCP. The accuracy of the object detection model is 97%, and the overall processing time of all the algorithms is 0.44949 seconds.

An Improved DCP-Based Image Defogging Algorithm Combined with Adaptive Fusion Strategy

Dark channel prior (DCP) has been widely used in single image defogging because of its simple implementation and satisfactory performance. This paper addresses the shortcomings of the DCP-based defogging algorithm and proposes an optimized method by using an adaptive fusion mechanism. This proposed method makes full use of the smoothing and “squeezing” characteristics of the Logistic Function to obtain more reasonable dark channels avoiding further refining the transmission map. In addition, a maximum filtering on dark channels is taken to improve the accuracy of dark channels around the object boundaries and the overall brightness of the defogged clear images. Meanwhile, the location information and brightness information of fog image are weighed to obtain more accurate atmosphere light. Quantitative and qualitative comparisons show that the proposed method outperforms state-of-the-art image defogging algorithms.

A Hybrid Dehazing Method and its Hardware Implementation for Image Sensors

The demand for image dehazing is ever-increasing in image sensor based outdoor systems such as self-driving vehicles, automatic driver assistance, and in highway monitoring and analytics. These applications require dedicated hardware solution to meet high frame-rate and low power constraints. Previously, a few prior based hardware dehazing methods have been presented. However, they produce artifacts in the restored images due to the failure of the underlying assumptions. To address this problem and to meet the stringent requirements, a data-driven image dehazing approach based on convolutional neural network (CNN) and dark channel prior (DCP) is proposed that automatically learns the important features and produces better results. The proposed method is hardware friendly and its hardware implementation is also presented. The design employs few line buffers to store activations and eliminates the requirement of off-chip memory like dynamic random-access memory (DRAM). Field programmable gate array (FPGA) and application specific integrated circuit (AISC) implementations show that the architecture is highly suitable for application scenarios with constrained computational resources, low memory, and tight power budget. The quantitative analysis shows more than 11% average PSNR improvement in image quality on standard datasets as compared to the state-of-the-art hardware methods while consuming comparable hardware resources and power.

Underwater Image Enhancement Method Based On Color Correction and Dark Channel Prior

Concerning to the problem in the distortion of color and the low contrast of underwater image, the image enhancement method in the underwater environment based on color correction and dark channel prior was proposed. When dealing with the color bias problem, the blue channel standard ratio is firstly calculated based on the blue channel, and the red and green channels of the underwater image are compensated to remove the blue and green background colors of the underwater image. In light of the problem in the low contrast of image in underwater environment, the dark channel prior (DCP) method based on the super pixel was used to enhance the corrected underwater image. Finally, the underwater object detection dataset images are tested, and the algorithm proposed in terms of the quality is made the comparison with six advanced image enhancement method in underwater environment. The experimental results show that the proposed algorithm earned the highest score in underwater quality evaluation index (UIQM) compared with the above algorithm.

Region Adaptive Single Image Dehazing.

Image haze removal is essential in preprocessing for computer vision applications because outdoor images taken in adverse weather conditions such as fog or snow have poor visibility. This problem has been extensively studied in the literature, and the most popular technique is dark channel prior (DCP). However, dark channel prior tends to underestimate transmissions of bright areas or objects, which may cause color distortions during dehazing. This paper proposes a new single-image dehazing method that combines dark channel prior with bright channel prior in order to overcome the limitations of dark channel prior. A patch-based robust atmospheric light estimation was introduced in order to divide image into regions to which the DCP assumption and the BCP assumption are applied. Moreover, region adaptive haze control parameters are introduced in order to suppress the distortions in a flat and bright region and to increase the visibilities in a texture region. The flat and texture regions are expressed as probabilities by using local image entropy. The performance of the proposed method is evaluated by using synthetic and real data sets. Experimental results show that the proposed method outperforms the state-of-the-art image dehazing method both visually and numerically.

Person re-identification using adversarial haze attack and defense: A deep learning framework

In this paper, the adversarial haze attack problem is addressed using the dark channel prior (DCP) de-hazing method. The adversarial attack affects rank-1 accuracy, where searching a target image against each test image is a specific search problem. To resolve this kind of problem, a feature fusion model is proposed to fuse handcrafted features and a pre-trained network model to obtain robust and discriminative features. The proposed model learns global features using transfer learning architecture whereas local features are obtained using the conventional method. Three pre-trained CNN models (AlexNet, ResNet, and Inception-v3) are used for feature extraction via transfer learning. The experiments are performed on publicly available datasets, achieving 68.6% accuracy in rank-1 with VIPER dataset and 79.6% accuracy with CHUK03 dataset. The proposed model enhances rank-1 accuracy of person re-identification when comparing with other state-of-the-art methods.

Single Image Dehazing Algorithm Based on Modified Dark Channel Prior

Single image dehazing algorithm based on Dark Channel Prior (DCP) is widely known. More and more image dehazing algorithms based on DCP have been proposed. However, we found that it is more effective to use DCP in the RAW images before the ISP pipeline. In addition, for the problem of DCP failure in the sky area, we propose an algorithm to segment the sky region and compensate the transmission. Extensive experimental results on both subjective and objective evaluation demonstrate that the performance of the modified DCP (MDCP) has been greatly improved, and it is competitive with the state-of-the-art methods.

Denoising, Edge Aware Restoration and Enhancement of Single Shallow Coastal Water Image

The dissolved particles and their resultant scattering are the underlying cause of low contrast and blur thereby producing poor quality underwater images. Single-shot shallow coastal underwater images are much in need of the preprocessing steps viz. image enhancement and restoration. The underwater image processing operations like classification, object detection and computer vision require an enhancement/restoration preprocessing. The paper aims to restore the visibility of objects in turbid water images with its effective edge aware restoration cum enhancement model. The restricted rolling guidance filter on the DCP-based restoration method produces better edge aware restoration and denoised output image. The model-based (MB) dark channel prior (DCP) along with an edge emphasis and contrast enhancement achieves the essential dehazing and improvement in contrast for the heavily blurred underwater images. The subjective and objective projections are an evidence for the same. This edge preserving and denoising nature of the model is also exhibited with comparisons made with promising algorithms over the decade.

A Study on the Dynamic Image-Based Dark Channel Prior and Smoke Detection Using Deep Learning

A Study on the Dynamic Image-Based Dark Channel Prior and Smoke Detection Using Deep Learning

Evaluation of the quality indicators in dehazed images: Color, contrast, naturalness, and visual pleasingness

Hazy images suffer from low quality due to blurring, veiling effects, and low contrast. To improve their visibility, dehazing methods attempt to restore them to their corresponding clear scenes, often by focusing more on obtaining an accurate estimate based on a known ground truth. The perceptual quality of dehazed images, which can be described by means of objective and subjective quality assessments, is often not considered. This paper provides a quality assessment of dehazed images, focusing on aspects, e.g., color, image structure, and naturalness. Four image dehazing methods are considered, i.e., Contrast Limited Adapted Histogram Equalization (CLAHE), Dark Channel Prior and Refinement (DCP-R), Perception Inspired Deep Dehazing Network with Refinement (PDR-Net) and Conditional Generative Adversarial Network (CGAN) Pix2pix. The dehazing results are then put through objective and subjective assessments, for a comprehensive evaluation on image quality. Overall, Pix2pix shows the best results objectively, excelling in the recovery of color and image structure. Although it is outperformed by DCP-R in terms of naturalness, our subjective assessment shows that Pix2pix is also most preferred by human observers.

Hybrid Approach for Image Defogging Process based on Atmospheric Light Estimation Process

Due to unfavorable weather circumstances, images captured from multiple sensors have limited the contrast and visibility. Many applications, such as web camera surveillance in public locations are used to identify object categorization and capture a vehicle's licence plate in order to detect reckless driving. The traditional methods can improve the image quality by incorporating luminance, minimizing distortion, and removing unwanted visual effects from the given images. Dehazing is a vital step in the image defogging process of many real-time applications. This research article focuses on the prediction of transmission maps in the process of image defogging through the combination of dark channel prior (DCP), transmission map with refinement, and atmospheric light estimation process. This framework has succeeded in the prior segmentation process for obtaining a better visualization. This prediction of transmission maps can be improved through the statistical process of obtaining higher accuracy for the proposed model. This improvement can be achieved by incorporating the proposed framework with an atmospheric light estimation algorithm. Finally, the experimental results show that the proposed deep learning model is achieving a superior performance when compared to other traditional algorithms.

Scene-Specific Dark Channel Prior for Single Image Fog Removal

Extensive presence of fog in outdoor images severely alters the scene appearance and hence reduces the visibility. Image processing based defogging algorithms are used to restore the details and colour in a single foggy image. Performances of the previous defogging approaches are considerably low since they fail to consider the image-specific cues. In this paper, a novel and simple defogging approach is proposed based on the estimation of depth map by considering the density of fog in local image regions. The proposed approach uses the scene-specific depth map information to compute the dark channel and transmission. The quality of recovered image is further improved by a post-processing technique. Experimental evaluation performed on FRIDA and FRIDA2 benchmark datasets demonstrates the proposed defogging framework outperforms state-of-the-art approaches. The code and the results of this work are open-sourced for reproducibility (https://github.com/RPRO5/Defogging).

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.