Dark Channel Research Articles

Unmanned aerial vehicles object detection based on image haze removal under sea fog conditions

Unmanned aerial vehicles (UAVs) have gradually become a major air threat to ships because of small size, good maneuverability, and low cost. Vision-based UAV detection offers one of the main ways to identify and protect against UAVs. Unlike land environment, the weather is complicated at sea. The visibility of an object is undermined by such factors as sea fog and sunlight, which makes it difficult to detect UAVs at sea through vision-based object detection. For the purpose of object detection at sea, this paper proposes a UAV object detection method based on image haze removal. In the proposed method, an improved dark channel haze removal (DCHR) algorithm is utilized to remove haze for and restore video images. Additionally, co-ordinate attention (CoordAttention, CA) is introduced to the lightweight algorithms of You Only Look Once (YOLO) for the object detection in restored video images, so as to improve the precision and speed of detection and reduce the miss rate. Some video images are also taken for detection experiments to verify the feasibility and effectiveness of the proposed method.

Chlorophyll detector development based on snapshot-mosaic multispectral image sensing and field wheat canopy processing

To achieve rapid nondestructive detection of chlorophyll content in crops in a field environment, we designed a new lightweight device based on a snapshot-mosaic imaging sensor given the sensitive properties of chlorophyll in the red-edge to near-infrared band (700–900 nm) and the advantages of imaging spectroscopy in environmental noise separation. The hardware part of the device includes a spectral camera based on a snapshot-mosaic sensor, a main control unit, a network module, a storage module, a power supply module, and a display and remote-control device. The software part was written using the Qt library and the C++ programming language. It includes the connection and initialization of the sensor, the control of the exposure time, the display of the image in real time, the acquisition and storage, and the calculation of the reflectance. The sensor was tested and calibrated to evaluate the sensor performance. Experimental results show that the different channels of the sensor have an excellent linear relationship for light intensity changes and can be used to measure reflected radiation from crop leaves in a field environment after calibration. At the same time, field experiments on wheat were conducted. A dark channel filtering method for 25 channels of the developed device was proposed to effectively eliminate the scattered light interference from the crop canopy in the spectral images. The background interference was effectively eliminated by background segmentation. Partial least squares regression (PLSR) method was used for modeling after preprocessing and band screening of the spectral data. The model achieved high accuracy with RC2 of 0.87, RV2 of 0.79, and RMSE of 3.94 mg/L. As a result, the system combined the proposed model with the developed device can effectively eliminate the interference of soil background and scattered light caused by the complex structure of crop canopy, and improve the accuracy of diagnosis. So that the system has potential in the crop growth variation analysis.

Haze Level Evaluation Using Dark and Bright Channel Prior Information

Haze level evaluation is highly desired in outdoor scene monitoring applications. However, there are relatively few approaches available in this area. In this paper, a novel haze level evaluation strategy for real-world outdoor scenes is presented. The idea is inspired by the utilization of dark and bright channel prior (DBCP) for haze removal. The change between hazy and haze-free scenes in bright channels could serve as a haze level indicator, and we have named it DBCP-I. The variation of contrast between dark and bright channels in a single hazy image also contains useful information to reflect haze level. By searching for a segmentation threshold, a metric called DBCP-II is proposed. Combining the strengths of the above two indicators, a hybrid metric named DBCP-III is constructed to achieve better performance. The experiment results on public, real-world benchmark datasets show the advantages of the proposed methods in terms of assessment accuracy with subjective human ratings. The study is first-of-its-kind with preliminary exploration in the field of haze level evaluation for real outdoor scenes, and it has a great potential to promote research in autonomous driving and automatic air quality monitoring. The open-source codes of the proposed algorithms are free to download.

Adaptive Bright and Dark Channel Combined with Defogging Algorithm Based on Depth of Field

Aiming at the problem that the dark channel prior algorithm is not ideal in removing fog at the distant view and the sudden change of depth, this paper proposes an adaptive bright and dark channel combined with defogging algorithm based on the depth of field. The algorithm divides the image into a distant area and a near area based on the depth of field. The near area uses the dark channel to remove the fog, and the distant area uses the bright channel to remove the fog, combined with the use of adaptive superpixel segmentation to give the content-based local area division and defogging control factor based on depth of field. It better solves the problem of large transmittance deviation and defogging distortion under content changes and sudden depth of field changes. From the subjective and objective indicators, the algorithm in this paper has achieved a good defogging effect, and the main indicators and comprehensive defogging quality are better than common algorithms.

Blind Image Deblurring via a Novel Sparse Channel Prior

Blind image deblurring (BID) is a long-standing challenging problem in low-level image processing. To achieve visually pleasing results, it is of utmost importance to select good image priors. In this work, we develop the ratio of the dark channel prior (DCP) to the bright channel prior (BCP) as an image prior for solving the BID problem. Specifically, the above two channel priors obtained from RGB images are used to construct an innovative sparse channel prior at first, and then the learned prior is incorporated into the BID tasks. The proposed sparse channel prior enhances the sparsity of the DCP. At the same time, it also shows the inverse relationship between the DCP and BCP. We employ the auxiliary variable technique to integrate the proposed sparse prior information into the iterative restoration procedure. Extensive experiments on real and synthetic blurry sets show that the proposed algorithm is efficient and competitive compared with the state-of-the-art methods and that the proposed sparse channel prior for blind deblurring is effective.

A new method for automated driving image defogging based on improved dark channel prior

To tackle the problem of blurred image subjects due to haze in the images captured by the automatic driving system, which affects the safety of automated driving, a new, improved dark channel image defogging method based on adaptive domain dark channel calculation, fast bilateral filtering to optimize transmittance and automatic color equalization is proposed. First, map the original image based on the haze image degradation model and evaluate the atmospheric light intensity and transmittance based on the adaptive domain. Then, the atmospheric transmittance is optimized by combining the powerful value filtering with the fast bilateral filtering method. Finally, the image is further optimized by using the multi-channel automatic color gradation equalization method to solve the phenomenon of oversaturated color and dark brightness in the filtered image. The results show that the algorithm of this paper has high clarity and contrast, reduces the computation and running time, preserves the image edge information, has an excellent fog removal effect, and is highly adaptive for automatic driving image processing.

Single underwater image restoration based on descattering and color correction

Underwater images typically exhibit color distortion and low contrast due to the absorption and scattering of the non-uniform medium during the propagation of light underwater. To address these problems, an effective underwater image restoration method is presented in this paper. Firstly, we adopted an adaptive dark channel prior that can be applied to underwater images with different color tones. Then, the local water light with strong robustness is obtained by utilizing maximum filtering and Gaussian low-pass filtering. Additionally, a linear relationship between blurred and clear images in the dark channel was introduced combining with the attenuation coefficient compensation to estimate the transmission of r, g and b channels. Ultimately, we designed a new adaptive color compensation method by taking into account the attenuation difference of the three color channels. Simulation and experimental results show that the proposed method can effectively improve the clarity and contrast of the image and eliminate color distortion.

A single image dehazing method based on decomposition strategy

Outdoor haze has adverse impact on outdoor image quality, including contrast loss and poor visibility. In this paper, a novel dehazing algorithm based on the decomposition strategy is proposed. It combines the advantages of the two-dimensional variational mode decomposition (2DVMD) algorithm and dark channel prior. The original hazy image is adaptively decomposed into low-frequency and high-frequency images according to the image frequency band by using the 2DVMD algorithm. The low-frequency image is dehazed by using the improved dark channel prior, and then fused with the high-frequency image. Furthermore, we optimize the atmospheric light and transmittance estimation method to obtain a defogging effect with richer details and stronger contrast. The proposed algorithm is compared with the existing advanced algorithms. Experiment results show that the proposed algorithm has better performance in comparison with the state-of-the-art algorithms.

DeSmoke-LAP: improved unpaired image-to-image translation for desmoking in laparoscopic surgery

PurposeRobotic-assisted laparoscopic surgery has become the trend in medicine thanks to its convenience and lower risk of infection against traditional open surgery. However, the visibility during these procedures may severely deteriorate due to electrocauterisation which generates smoke in the operating cavity. This decreased visibility hinders the procedural time and surgical performance. Recent deep learning-based techniques have shown the potential for smoke and glare removal, but few targets laparoscopic videos.MethodWe propose DeSmoke-LAP, a new method for removing smoke from real robotic laparoscopic hysterectomy videos. The proposed method is based on the unpaired image-to-image cycle-consistent generative adversarial network in which two novel loss functions, namely, inter-channel discrepancies and dark channel prior, are integrated to facilitate smoke removal while maintaining the true semantics and illumination of the scene.ResultsDeSmoke-LAP is compared with several state-of-the-art desmoking methods qualitatively and quantitatively using referenceless image quality metrics on 10 laparoscopic hysterectomy videos through 5-fold cross-validation.ConclusionDeSmoke-LAP outperformed existing methods and generated smoke-free images without applying ground truths (paired images) and atmospheric scattering model. This shows distinctive achievement in dehazing in surgery, even in scenarios with partial inhomogenenous smoke. Our code and hysterectomy dataset will be made publicly available at https://www.ucl.ac.uk/interventional-surgical-sciences/weiss-open-research/weiss-open-data-server/desmoke-lap.

Underwater image restoration by red channel compensation and underwater median dark channel prior.

Underwater images often show low contrast, blurring, and color distortion due to the absorption and scattering of light. In contrast to existing underwater image restoration methods, we propose an underwater image restoration method with red channel compensation and blue-green channel restoration. First, a proposed approach relies on the hue and attenuation differences between different color channels of the underwater image to estimate the background light. Then, the red channel is enhanced according to a perfect reflection assumption algorithm. Finally, a new median underwater dark channel prior (MUDCP) is proposed to precisely estimate the blue-green channel transmission map. Experimental results show that our method significantly improves contrast, removes color bias, and preserves more detail than other underwater restoration techniques.

Maritime video defogging based on spatial-temporal information fusion and an improved dark channel prior

Maritime video defogging based on spatial-temporal information fusion and an improved dark channel prior

Subjective and Objective Quality Evaluation for Underwater Image Enhancement and Restoration

Since underwater imaging is affected by the complex water environment, it often leads to severe distortion of the underwater image. To improve the quality of underwater images, underwater image enhancement and restoration methods have been proposed. However, many underwater image enhancement and restoration methods produce over-enhancement or under-enhancement, which affects their application. To better design underwater image enhancement and restoration methods, it is necessary to research the underwater image quality evaluation (UIQE) for underwater image enhancement and restoration methods. Therefore, a subjective evaluation dataset for an underwater image enhancement and restoration method is constructed, and on this basis, an objective quality evaluation method of underwater images, based on the relative symmetry of underwater dark channel prior (UDCP) and the underwater bright channel prior (UBCP) is proposed. Specifically, considering underwater image enhancement in different scenarios, a UIQE dataset is constructed, which contains 405 underwater images, generated from 45 different underwater real images, using 9 representative underwater image enhancement methods. Then, a subjective quality evaluation of the UIQE database is studied. To quantitatively measure the quality of the enhanced and restored underwater images with different characteristics, an objective UIQE index (UIQEI) is used, by extracting and fusing four groups of features, including: (1) the joint statistics of normalized gradient magnitude (GM) and Laplacian of Gaussian (LOG) features, based on the underwater dark channel map; (2) the joint statistics of normalized gradient magnitude (GM) and Laplacian of Gaussian (LOG) features, based on the underwater bright channel map; (3) the saturation and colorfulness features; (4) the fog density feature; (5) the global contrast feature; these features capture key aspects of underwater images. Finally, the experimental results are analyzed, qualitatively and quantitatively, to illustrate the effectiveness of the proposed UIQEI method.

Underwater Image Restoration via DCP and Yin–Yang Pair Optimization

Underwater image restoration is a challenging problem because light is attenuated by absorption and scattering in water, which can degrade the underwater image. To restore the underwater image and improve its contrast and color saturation, a novel algorithm based on the underwater dark channel prior is proposed in this paper. First of all, in order to reconstruct the transmission maps of the underwater image, the transmission maps of the blue and green channels are optimized by the proposed first-order and second-order total variational regularization. Then, an adaptive model is proposed to improve the first-order and second-order total variation. Finally, to solve the problem of the excessive attenuation of the red channel, the transmission map of the red channel is compensated by Yin–Yang pair optimization. The simulation results show that the proposed restored algorithm outperforms other approaches in terms of the visual effects, average gradient, spatial frequency, percentage of saturated pixels, underwater color image quality evaluation and evaluation metric.

Guiding the optical vortex along predesigned parabolic trajectories from circular symmetric Airy-like beams.

In this paper, by phase-modulating an optical wavefront on circular symmetric Airy vortex beams, we present the circular symmetric Airy-like vortex beams propagating along predesigned parabolic trajectories. Our result shows that we can realize the propagation of an optical vortex with a closed ring lobe along an accelerating parabolic trajectory within a certain propagation distance by using this kind of phase-modulated circular symmetric Airy beam. The vortex that is superimposed on the beams is able to reproduce after being blocked. Additionally, a single twisted dark channel or multiple tornado dark channels with orbital angular momentum rotating along the predesigned parabolic trajectories can also be formed when we impose the off-axis optical vortex on this kind of beam, which has potential in applications of light capturing.

A Study of Double Dark Photons Produced by Lepton Colliders using High Performance Computing

The universe is thought to be filled with not only Standard Model (SM) matters but also dark matters. Dark matter is thought to play a major role in its construction. However, the identity of dark matter is as yet unknown, with various search methods from astrophysical observartion to particle collider experiments. Because of the cross-section that is a thousand times smaller than SM particles, dark matter research requires a large amount of data processing. Therefore, optimization and parallelization in High Performance Computing is required. Dark matter in hypothetical hidden sector is though to be connected to dark photons which carries forces similar to photons in electromagnetism. In the recent analysis, it was studied using the decays of a dark photon at collider experiments. Based on this, we studies double dark photon decays at lepton colliders. The signal channels are e+e– → AʹAʹ and e+e– → AʹAʹγ where dark photon Aʹ decays dimuon. These signal channels are based on the theory that dark photons only decay into heavily charged leptons, which can explain the muon magnetic momentum anomaly. We scanned the cross-section according to the dark photon mass in experiments. MadGraph5 was used to generate events based on a simplified model. Additionally, to get the maximum expected number of events for the double dark photon channel, the detector efficiency for several center of mass (CM) energy were studied using Delphes and MadAnalysis5 for performance comparison. The results of this study will contribute to the search for double dark photon channels at lepton colliders.

Unsupervised single image dehazing with generative adversarial network

Most recent learning algorithms for single image dehazing are designed to train with paired hazy and corresponding ground truth images, typically synthesized images. Real paired datasets can help to improve performance, but are tough to acquire. This paper proposes an unsupervised dehazing algorithm based on GAN to alleviate this issue. An end-to-end network based on GAN architecture is established and fed with unpaired clean and hazy images, signifying that the estimation of atmospheric light and transmission is not required. The proposed network consists of three parts: a generator, a global test discriminator, and a local context discriminator. Moreover, a dark channel prior based attention mechanism is applied to handle inconsistency haze. We conduct experiments on RESIDE datasets. Extensive experiments demonstrated the effectiveness of the proposed approach which outperformed previous state-of-the-art unsupervised methods by a large margin.

Clarity method of fog and dust image in fully mechanized mining face

At present, the abnormal state of equipment and surrounding rocks in the fully mechanized mining face is mainly detected by visual methods. However, the vision sensor works in a low-light environment and it is affected by factors such as water fog and dust, which lead to blurred images. The defogging algorithm of image based on boundary constraint and context regularization has a good effect on image restoration in the daily environment, but the recovery quality is poor in low illumination environment. Therefore, a method based on boundary constraint and nonlinear context regularization is proposed. The model of fog and dust image is established, and the transmittance function is roughly estimated by boundary constraint method. Then, the nonlinear context regularization method based on logarithmic transformation is used to estimate and optimize the scene transmission model to improve the brightness of the image, and the low illumination fog and dust image is restored by the optimized transmittance function. The logarithmic transformation multiple is selected according to the peak value of image brightness. In order to highlight the effectiveness of our method, the widely used and improved Dark Channel Prior or other methods are used for comparison. The experiment results indicate that our method can effectively remove fog and dust and improve the brightness of the image of the fully mechanized face. It is of great significance to ensure safe production and safety of workers and equipment in coal mine.

Image Dehazing Using LiDAR Generated Grayscale Depth Prior.

In this paper, the dehazing algorithm is proposed using a one-channel grayscale depth image generated from a LiDAR point cloud 2D projection image. In depth image-based dehazing, the estimation of the scattering coefficient is the most important. Since scattering coefficients are used to estimate the transmission image for dehazing, the optimal coefficients for effective dehazing must be obtained depending on the level of haze generation. Thus, we estimated the optimal scattering coefficient for 100 synthetic haze images and represented the distribution between the optimal scattering coefficient and dark channels. Moreover, through linear regression of the aforementioned distribution, the equation between scattering coefficients and dark channels was estimated, enabling the estimation of appropriate scattering coefficient. Transmission image for dehazing is defined with a scattering coefficient and a grayscale depth image, obtained from LiDAR 2D projection. Finally, dehazing is performed based on the atmospheric scattering model through the defined atmospheric light and transmission image. The proposed method was quantitatively and qualitatively analyzed through simulation and image quality parameters. Qualitative analysis was conducted through YOLO v3 and quantitative analysis was conducted through MSE, PSNR, SSIM, etc. In quantitative analysis, SSIM showed an average performance improvement of 24%.

Image defogging system and algorithm research based on MATLAB platform

Since the Reform and Opening up, the characteristics of China’s high-quality economic development have become more distinctive, the adjustment of economic and social structure has been continuously optimized, the kinetic energy has been continuously enhanced, and the degree of industrialization has been significantly improved. However, it has also caused some environmental problems, and haze pollution is one of them. The haze weather with low visibility has a certain impact on people’s daily travel, transportation and navigation. Because the images taken under the haze condition will be degraded to varying degrees and the image details will be lost, the effect of identifying and processing the degraded images in the later stage will be reduced. Therefore, in order to clear and restore the degraded image, we developed a defogging system based on MATLAB software. The system mainly uses histogram equalization, MSR and dark channel a priori defogging algorithms to restore fog degraded images. The results indicate that various defogging algorithms are able to enhance the clarity of the picture to varying degrees. The successful implementation of the defogging system lays a theoretical foundation for the in-depth research and application of fog image defogging processing technology, and has good technical operability.

Eliminación de los efectos de humo en cirugía laparoscópica usando redes antagónicas generativas y el principio del canal oscuro

In laparoscopic surgery, image quality can be severely degraded by surgical smoke caused by the use of tissue dissection tools that reduce the visibility of the observed organs and tissues. Improve visibility in laparoscopic surgery by combining image processing techniques based on classical methods and artificial intelligence. Development of a hybrid approach to eliminating the effects of surgical smoke, based on the combination of the dark channel prior (DCP) method and a pixel-to-pixel neural network architecture known as a generative adversarial network (GAN). Experimental results have shown that the proposed method achieves better performance than individual DCP and GAN results in terms of restoration quality, obtaining (according to PSNR and SSIM index metrics) better results than some related state-of-the-art methods. The proposed approach decreases the risks and time of laparoscopic surgery because once the network is trained, the system can improve real-time visibility.