Defogging Algorithm Research Articles

A Comprehensive Review of Traditional and Deep-Learning-Based Defogging Algorithms

Images captured under adverse weather conditions often suffer from blurred textures and muted colors, which can impair the extraction of reliable information. Image defogging has emerged as a critical solution in computer vision to enhance the visual quality of such foggy images. However, there remains a lack of comprehensive studies that consolidate both traditional algorithm-based and deep learning-based defogging techniques. This paper presents a comprehensive survey of the currently proposed defogging techniques. Specifically, we first provide a fundamental classification of defogging methods: traditional techniques (including image enhancement approaches and physical-model-based defogging) and deep learning algorithms (such as network-based models and training strategy-based models). We then delve into a detailed discussion of each classification, introducing several representative image fog removal methods. Finally, we summarize their underlying principles, advantages, disadvantages, and give the prospects for future development.

Analysis of the Generalization Ability of Defogging Algorithms on RICE Remote Sensing Images.

This paper explores the generalization ability of defogging algorithms on RICE (A Remote Sensing Image Dataset for Cloud Removal) remotely sensed images. RICE is a dataset of remotely sensed images used for removing clouds, allowing the researcher to better evaluate the performance of defogging algorithms for cloud removal from remotely sensed images. In this paper, four classical defogging algorithms, including AOD-Net, FFA-Net, dark channel prior, and DehazeFormer, are selected and applied to the task of de-cloud in RICE remote sensing images. The performance of these algorithms on the RICE dataset is analyzed by comparing the experimental results, and their differences, advantages, and disadvantages in dealing with de-clouded remote sensing images are explored. The experimental results show that the four defogging algorithms are capable of performing well on uniform thin cloud images, but there is a color distortion and the performance is weak when it comes to inhomogeneous clouds as well as thick clouds. So, the generalization ability of the algorithms is weak when the defogging algorithms are applied to the problem of cloud removal. Finally, this paper proposes improvement ideas for the de-cloud problem of RICE remote sensing images and looks forward to possible future research directions.

Evaluation of histogram image defogging methods based on histogram equalization, dark channel prior, and convolutional neural network

Air pollution condition is getting worse with the advancement of society development, environmental pollution has gradually intensified, and smog frequently occurs in more and more cities. On foggy day, the saturation and contrast of an image could be low, and colors tend to drift and distortion. As a result, seeking a simple and effective image de-fogging technique is important for the subsequent research. In this study, three existing classical de-fogging algorithms are reproduced: histogram equalization, dark channel prior method, and convolutional neural network. The three de-fogging algorithms were compared respectively under the conditions of thin fog, thick fog, high brightness, and low brightness, so as to analyze their advantages and disadvantages. It is concluded that there is no obvious difference among the three algorithms in the de-fogging effect under the conditions of thick fog and high brightness, but relatively speaking, the de-fogging image generated by the dark channel prior is more real. When the fog is thin, the dark channel prior and convolutional neural network work better. Under the condition of low brightness, the histogram equalization has a better de-fogging effect.

YOLO-GW: Quickly and Accurately Detecting Pedestrians in a Foggy Traffic Environment.

In practice, the object detection algorithm is limited by a complex detection environment, hardware costs, computing power, and chip running memory. The performance of the detector will be greatly reduced during operation. Determining how to realize real-time, fast, and high-precision pedestrian recognition in a foggy traffic environment is a very challenging problem. To solve this problem, the dark channel de-fogging algorithm is added to the basis of the YOLOv7 algorithm, which effectively improves the de-fogging efficiency of the dark channel through the methods of down-sampling and up-sampling. In order to further improve the accuracy of the YOLOv7 object detection algorithm, the ECA module and a detection head are added to the network to improve object classification and regression. Moreover, an 864 × 864 network input size is used for model training to improve the accuracy of the object detection algorithm for pedestrian recognition. Then the combined pruning strategy was used to improve the optimized YOLOv7 detection model, and finally, the optimization algorithm YOLO-GW was obtained. Compared with YOLOv7 object detection, YOLO-GW increased Frames Per Second (FPS) by 63.08%, mean Average Precision (mAP) increased by 9.06%, parameters decreased by 97.66%, and volume decreased by 96.36%. Smaller training parameters and model space make it possible for the YOLO-GW target detection algorithm to be deployed on the chip. Through analysis and comparison of experimental data, it is concluded that YOLO-GW is more suitable for pedestrian detection in a fog environment than YOLOv7.

Improved color image defogging algorithm based on dark channel prior

An improved algorithm of image defogging was proposed based on dark channel prior in order to solve the low efficiency and color distortion in the bright area using original algorithm. If the image contains large areas of bright areas such as sky, white clouds or partial white objects and water surface, we can know that the dark channel prior theory does not apply to these areas. Firstly, it is necessary to clear the bright area of the image. According to principle that he adjacent pixel attributes have similarity, the image transmittance of the local region also has similarity, Block function is Consruted. Applied the dark channel prior, judging whether each block includes a bright area by the absolute value of difference of atmospheric intensity and dark channel, the dark and bright areas of the image are obtained. So the estimation value of the adaptive space transmittance are also obtained. Secondly, the transmittance of bright region is small and it causes deviation, so the enhancement formula is used to modify it dynamically. In order to preserve the edge details after image restoration, for bright areas, using texture function to optimize transmittance independently, for others, using gradient and texture function together. Finally, it restored the fog-free image applying the atmospheric scattering model. The experimental results showed that the restored image had obvious details and rich color and fast processing speed through the proposed algorithm. The algorithm can also be applied to outdoor visual systems, such as video surveillance, intelligent traffic and so on.

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.

A polarization-based image restoration method for both haze and underwater scattering environment

Existing polarization-based defogging algorithms rely on the polarization degree or polarization angle and are not effective enough in scenes with little polarized light. In this article, a method of image restoration for both haze and underwater scattering environment is proposed. It bases on the general assumption that gray variance and average gradient of a clear image are larger than those of an image in a scattering medium. Firstly, based on the assumption, polarimetric images with the maximum variance (Ibest) and minimum variance (Iworst) are calculated from the captured four polarization images. Secondly, the transmittance is estimated and used to remove the scattering light from background medium of Ibest and Iworst. Thirdly, two images are fused to form a clear image and the color is also restored. Experimental results show that the proposed method obtains clear restored images both in haze and underwater scattering media. Because it does not rely on the polarization degree or polarization angle, it is more universal and suitable for scenes with little polarized light.

Image Defogging Algorithm Based on Sparse Representation

Aiming at the problems of low contrast and low definition of fog degraded image, this paper proposes an image defogging algorithm based on sparse representation. Firstly, the algorithm transforms image from RGB space to HSI space and uses two-level wavelet transform extract features of image brightness components. Then, it uses the K-SVD algorithm training dictionary and learns the sparse features of the fog-free image to reconstructed I-components of the fog image. Using the nonlinear stretching approach for saturation component improves the brightness of the image. Finally, convert from HSI space to RGB color space to get the defog image. Experimental results show that the algorithm can effectively improve the contrast and visual effect of the image. Compared with several common defog algorithms, the percentage of image saturation pixels is better than the comparison algorithm.

A cascaded approach for image defogging based on physical and enhancement models

In this paper, we propose a novel cascade strategy approach for visibility restoration in foggy images. The proposed cascade strategy is based on the combination of enhancement and physical models, the contrast limited adaptive histogram equalization (CLAHE) and no-black pixel constraint with planar assumption (NBPC $$+$$ PA) methods. The use of CLAHE enhances the visibility of foggy image, but it produces color and edge distortion, boosts noise and creates halo effects. We overcome these shortcomings of CLAHE by feeding its output to the NBPC $$+$$ PA. In order to improve the cascaded performance of the two methods, we determine the suitable parameters. The proposed cascading utilizes the individual strengths of the two approaches which in turn provides better defogging results for homogeneous as well as inhomogeneous fog. We present objective quality assessment and visibility enhancement of various foggy images. The experimental results verify the enhanced defogging capabilities of the proposed cascade strategy compared to the existing defogging algorithms.

Improved Dark Channel Defogging Algorithm for Defect Detection in Underwater Structures

Underwater structures are crucial for national economic and social development. However, because of their complex environment, they are susceptible to damage during service. This damage should be prevented to minimize casualties and economic loss. Therefore, this study investigates the problems of disease identification and area statistics of underwater structures. To this end, the Dark‐Retinex (DR) algorithm that can enhance the image of underwater structure defects is proposed. The algorithm consists of a combination of a dark channel algorithm and the Retinex algorithm. This study analyzes the current research status of underwater image processing technology, designs the overall framework of the DR algorithm, and uses the underwater structure disease image to verify the algorithm. Comparing the effect of the image with only the dark channel defogging and DR algorithm processing, the DR algorithm is observed to achieve “defogging” processing of underwater structural disease images to achieve better enhancement effects. Moreover, for accurate disease area statistics, the binary morphology and optimal threshold segmentation theories are combined to perform disease edge screening and remove interference information. Finally, accurate statistics of the proportion of diseased pixels are achieved, as well as the quantitative detection of surface diseases of underwater structures. After actual operational verification, the improved image dehazing and parallel boundary screening algorithms can achieve better application results to detect underwater structure defects and disease statistics. The objective evaluation shows that the DR algorithm facilitates image processing, can obtain relatively high‐quality target images, and can solve the problems of time‐consuming and labor‐intensive detection of underwater structures, with significant risks and limitations. This helps pave the way for (1) the actual engineering of surface structure detection of underwater structures, (2) future storage in the database and assessment of hazard levels, and (3) a guide for engineering technicians to take corresponding maintenance measures.

一种偏振普适性多尺度实时的图像去雾算法

一种偏振普适性多尺度实时的图像去雾算法

An Improved Defogging Algorithm Based on Dark Color Theory Combined with Self-Adaptive Threshold Mechanism

Defogging algorithms based on dark channel prior have color shift in light color areas because of inaccurate estimation of transmittance. To resolve this problem, a novel improved image clearness method is proposed. Based on the dark channel prior, the essential causes of color shift are analyzed, with two important factors summarized. Then, transmission map is calculated by using 3⁎3 fixed region, and the restoration module based on self-adaptive threshold mechanism for transmission map is provided. Some experiments are carried out to determine parameters in restoration module to correct the transmission map. According to the corrected transmission map, a transmission restoration algorithm is constructed based on the self-adaptive threshold mechanism to improve the performance of the fog-free image. The experiment results show that this method can resolve the color shift in light color areas effectively and guarantee the overall framework of defogging method based on dark color theory unchanged.

Adaptive Demisting Algorithm Based on Polari metric Image

Aiming at the problem of high complexity and poor robustness of the defogging algorithm of the existing polarized image, an adaptive de-fogging algorithm based on the polarized image is proposed. Based on the principle of image dark channel, the algorithm selects minimum mean square filter to automatically extract the sky region, and obtains the radiation intensity information after scene restoration. Experimental results show that the proposed algorithm can effectively improve the sharpness of the polarized image and increase the robustness of the algorithm.

Review of Video and Image Defogging Algorithms and Related Studies on Image Restoration and Enhancement

Video and images acquired by a visual system are seriously degraded under hazy and foggy weather, which will affect the detection, tracking, and recognition of targets. Thus, restoring the true scene from such a foggy video or image is of significance. The main goal of this paper was to summarize current video and image defogging algorithms. We first presented a review of the detection and classification method of a foggy image. Then, we summarized existing image defogging algorithms, including image restoration algorithms, image contrast enhancement algorithms, and fusion-based defogging algorithms. We also presented current video defogging algorithms. We summarized objective image quality assessment methods that have been widely used for the comparison of different defogging algorithms, followed by an experimental comparison of various classical image defogging algorithms. Finally, we presented the problems of video and image defogging which need to be further studied. The code of all algorithms will be available at http://www.yongxu.org/lunwen.html .

A Defogging Algorithm Based on Statistical Region Merging

Different regions in Foggy images have different contrast attenuations. This paper presented a defogging algorithm based on Statistical Region Merging. Firstly, we segmented the image by Mean Shift; Secondly, we merged these divided regions through Statistical Region Merging; Finally, we enhanced the contrast of different regions by Dynamic Segmentation Histogram Equalization contrast enhancement. The experimental results show that our defogging method is more effective than other methods.

국부영역 색포화 평가 방법을 통한 안개제거 알고리즘

본 논문은 먼지, 물방울, 안개 등과 같이 빛의 산란 및 감쇄를 발생시키는 외부요인에 의해 열화된 영상의 화질을 향상시키기 위한 새로운 방법을 제시한다. 기존의 안개제거 방법들은 전역대기 산란광의 오추정 및 매개체 전달량 오류로 인하여 영상을 복원하였을 때 명암대비가 낮거나 일부 영역에서 색이 왜곡되는 문제가 발생한다. 따라서 본 논문에서는 이를 개선하기 위해 강인한 전역대기 산란광 추정방법과 국부영역 색포화 평가방법을 통한 매개체 전달량 추정방법을 제안한다. 제안하는 방법은 전역대기 산란광 오추정에 기인하여 발생하는 매개체 전달량 오류와 색포화 문제를 개선하였으며, 이를 통해 복원영상의 화질을 향상시켰다. 제안하는 방법과 기존 방법과의 비교 실험결과는 전역대기 산란광 추정의 정확성과 복원된 영상의 객관적, 주관성 성능 향상을 보여준다.

The Research on Defogging Algorithm Based on Dark Channel in Color Space

In order to restore degraded image quality under foggy conditions, this paper proposes an improved defogging algorithm based on dark color knowledge in color space. According to the scene depth relationship between objects in a foggy image, the algorithm can repair the dark color figure, in which the grey scale line distance is used to judge edges, the most similar point of dark colors is selected as new value of dark value based on the character of the adjacent dark color, and the dark color image is used to obtain atmospheric light value automatically. Finally, the defogging purposes have been achieved based on the inverse process of the model. Experimental results show that improved algorithm effectively improves the visual effect of fog-degraded images.

Image Defogging Based on Fog Veil Subtraction

In this paper, we propose a new defog algorithm based on fog veil subtraction to remove fog from a single image. The proposed algorithm first estimates the illumination component of the image by applying smoothing to the degraded image, and then obtains the uniform distributed fog veil through a mean calculation of the illumination component. Next, we multiply the uniform veil by the original image to obtain a depth-like map and extract its intensity component to produce a fog veil whose distribution is according with real fog density of the scene. Once the fog veil is calculated, the reflectance map can be obtained by subtracting the veil from the degraded image. Finally, we apply an adaptive contrast stretching to the reflectance map to obtain an enhanced result. This algorithm can be easily extended to video domains and is verified by both real-scene photographs and videos.