Fog Removal Algorithm Research Articles

Fog Removal Algorithm for Geographic Images Using Generative Adversarial Nets

In order to improve the imaging effect of geographic images in a complex weather environment. This paper designs a geographic image defogging algorithm by using the generation countermeasure network to improve the clarity of the image. Firstly, the imaging mode and degradation process of the foggy image is studied in this paper, and the atmospheric scattering model is established to simulate the degradation of the image. Secondly, according to the data characteristics of image defogging, an optimized image defogging model is generated based on Conditional Generation Adversarial Nets (CGAN), so that the image output by the model is closer to the real image. The experimental results show that the image defogging model designed in this paper has good contrast and color richness, which can effectively solve the problem of image degradation in fog. Moreover, the reduction of paired data sets will reduce the measurement index of the geographic image defogging model. This paper provides a reference for the study of geographic image defogging.

Image Defogging Algorithm Based on Sky Region Segmentation and Dark Channel Prior

AbstractBased on image segmentation and the dark channel prior, this paper proposes a fog removal algorithm in the HSI color space. Usually, the dark channel prior based defogging methods easily produce color distortion and halo effect when applied on images with a large sky area, because the sky region does not meet the prior assumption. For this reason, our method presents a new threshold sky region segmentation algorithm using the initial transmission map of the intensity component I. Based on the segmentation result, the initial transmission map is modified in turn, and finally refined by the guided filter. The saturation components S is reconstructed using the low frequencies of the V-transform to reduce noise, and stretched by multiplying a constant related to the initial transmission map. Experimental results show that the proposed algorithm has low time complexity and compelling fog removal result in both visual effect and quantitative measurement.

Histogram Equalization Based Defogging of Fogged Image

Foggy images causes contrast loss and color distortion and fog changes the visibility of scene. Fog removal algorithm used in tracking and navigation, consumer electronics, and entertainment industries. Many algorithms are proposed for efficient fog removal. Existing defogging methods cannot produce accurate scene transmission in removable of their unpleasing distortion and it is high complexity. In this paper, fog removal algorithms/techniques are represented for image processing. The overall objective of this paper is to explore images from fogged image by using histogram equalization. In the end, the proposed method is tested on several foggy images.

Research of Polarized Image Defogging Technique Based on Dark Channel Priori and Guided Filtering

In fog weather, aiming at the poor performance of fog removal based on the conventional polarization defogging algorithm, a dense fog removal algorithm based on the polarization principle of dark channel priori and guided filtering is proposed. The polarization information is combined with the primal theory of dark primary for the purpose of combining the polarization information with the defogging model. Then the dark channel principle of the image is obtained using atmospheric scattering model. Finally, the atmosphere is corrected by the guidance filtering algorithm. The experimental results show that the standard deviation, entropy and average gradient of the defogged image are much higher than those of the existing polarization defogging technology. This method can effectively enhance the overall contrast of the image under foggy weather and improve the target recognition ability of polarized images.

Dark Channel Prior을 이용한 LabVIEW 기반의 동영상 안개제거

LabVIEW coding for video dehazing was developed. The dark channel prior proposed by K. He was applied to remove fog based on a single image, and K. B. Gibson's median dark channel prior was applied, and implemented in LabVIEW. In other words, we improved the image processing speed by converting the existing fog removal algorithm, dark channel prior, to the LabVIEW system. As a result, we have developed a real-time fog removal system that can be commercialized. Although the existing algorithm has been utilized, since the performance has been verified real - time, it will be highly applicable in academic and industrial fields. In addition, fog removal is performed not only in the entire image but also in the selected area of the partial region. As an application example, we have developed a system that acquires clear video from the long distance by connecting a laptop equipped with LabVIEW SW that was developed in this paper to a 100~300 times zoom telescope.

Improved Haze Removal of Underwater Images using Particle Swarm Optimization

The main objective of fog removal algorithm is to estimate the airlight map for the given image and then perform the necessary operations on the image in order to overcome the fog in the image and enhance the quality of the image. The dark channel prior method of fog removal is more suitable and time-saving in real-time systems. In this paper, an efficient approach for fog removal of foggy images based on the combination of dark channel prior and genetic algorithm is presented. It is found that the proposed method is more suitable for obtaining the better quality of the image than the most of the existing methods.

Combating Bad Weather Part II: Fog Removal from Image and Video

Every year lives and properties are lost in road accidents. About one-fourth of these accidents are due to low vision in foggy weather. At present, there is no algorithm that is specifically designed for the removal of fog from videos. Application of a single-image fog removal algorithm over each video frame is a time-consuming and costly affair. It is demonstrated that with the intelligent use of temporal redundancy, fog removal algorithms designed for a single image can be extended to the real-time video application. Results confirm that the presented framework used for the extension of the fog removal algorithms for images to videos can reduce the complexity to a great extent with no loss of perceptual quality. This paves the way for the real-life application of the video fog removal algorithm. In order to remove fog, an efficient fog removal algorithm using anisotropic diffusion is developed. The presented fog removal algorithm uses new dark channel assumption and anisotropic diffusion for the initialization and refinement of the airlight map, respectively. Use of anisotropic diffusion helps to estimate the better airlight map estimation. The said fog removal algorithm requires a single image captured by uncalibrated camera system. The anisotropic diffusion-based fog removal algorithm can be applied in both RGB and HSI color space. This book shows that the use of HSI color space reduces the complexity further. The said fog removal algorithm requires pre- and post-processing steps for the better restoration of the foggy image. These pre- and post-processing steps have either data-driven or constant parameters that avoid the user intervention. Presented fog removal algorithm is independent of the intensity of the fog, thus even in the case of the heavy fog presented algorithm performs well. Qualitative and quantitative results confirm that the presented fog removal algorithm outperformed previous algorithms in terms of perceptual quality, color fidelity and execution time. The work presented in this book can find wide application in entertainment industries, transportation, tracking and consumer electronics. Table of Contents: Acknowledgments / Introduction / Analysis of Fog / Dataset and Performance Metrics / Important Fog Removal Algorithms / Single-Image Fog Removal Using an Anisotropic Diffusion / Video Fog Removal Framework Using an Uncalibrated Single Camera System / Conclusions and Future Directions / Bibliography / Authors' Biographies

Evaluation of a New Integrated Fog Removal Algorithm IDCP with Airlight

This paper has proposed a new fog removal technique IDCP which will integrate dark channel prior with CLAHE and adaptive gamma correction to remove the fog from digital images. Fog in image reduces the visibility of the digital images. Poor visibility not only degrades the perceptual image quality but it also affects the performance of computer vision algorithms such as object detection, tracking, surveillance and segmentation.Various factors such as fog, mist and haze caused by the water droplets present in the air during bad weather leads to poor visibility. The proposed algorithm is designed and implemented in MATLAB using image processing toolbox. The comparison among Air-light and the proposed algorithm is also drawn based upon certain performance parameters. The comparison analysis has shown that the proposed algorithm has shown quite effective results. Index terms—Visibility restoration, Air-light map, Fog removal and Haze.

A new single-image fog removal algorithm based on physical model

Based on the physical model of atmospheric scattering and the optical reflectance imaging model, three major factors which affect the effect of fog removal are discussed in detail, dark channel phenomenon is explained via the optical model, and an approach for solving the parameter in the atmospheric scattering model is rigorously derived from a new perspective. Using gray-scale opening operation and fast joint bilateral filtering techniques, the proposed algorithm can effectively obtain the global atmospheric light and greatly improve the speed and accuracy of atmospheric scattering function solving. Finally, the scene albedo is recovered by inverting this model. Compared with existing algorithms, complexity of the proposed method is only a linear function of the number of input image pixels and this allows a very fast implementation. The simulation results show that the processing time of images with a resolution of 576*768 is only 1.7s; Results on a variety of outdoor foggy images demonstrate that the proposed method achieves good restoration for contrast and color fidelity, resulting in a great improvement in image visibility.

Efficient fog removal from video

In this paper, a framework of real-time video processing for fog removal using uncalibrated single camera system is proposed. Intelligent use of temporal redundancy present in video frames paves the way for real-time implementation. Any fog removal algorithm for images acquired with uncalibrated single camera system can be extended to video using the proposed framework. For the purpose of real-time implementation, several fog removal algorithms for images are investigated and few top ranking algorithms in speed and quality are chosen. Simulation results confirm that proposed framework reduces the computation per frame significantly. Proposed fog removal framework has a wide application in navigation, transportation, and other industries.

Removal of Fog from Images: A Review

In this paper, reported algorithms for the removal of fog are reviewed. Fog reduces the visibility of scene and thus performance of various computer vision algorithms which use feature information. Formation of fog is the function of the depth. Estimation of depth information is under constraint problem if single image is available. Hence, removal of fog requires assumptions or prior information. Fog removal algorithms estimate the depth information with various assumptions, which are discussed in detail here. Fog removal algorithm has a wide application in tracking and navigation, consumer electronics, and entertainment industries.

Single image fog removal using anisotropic diffusion

In this study, a novel and efficient fog removal algorithm is proposed. Fog formation is because of attenuation and airlight. Attenuation reduces the contrast and airlight increases the whiteness in the scene. Proposed algorithm uses anisotropic diffusion to recover scene contrast. Simulation results demonstrate that proposed algorithm outperforms prior state-of-the-art algorithms in terms of contrast gain, percentage of number of saturated pixels and computation time. Proposed algorithm is independent of the density of fog and does not require user intervention. It can handle colour as well as grey images. Along with the RGB (red, blue and green) colour model, proposed algorithm can work for HSI (hue, saturation and intensity) model which further reduces the computation. Proposed algorithm has a wide application in tracking and navigation, consumer electronics and entertainment industries.