Object Detection in Fog Degraded Images

2004

Image contrast often significantly suffers from degradation due to haze, fog or mist spread in atmosphere, and adds more atmospheric light that harms the visibility of image. In this paper, various methods for reduction of fog have been analyzed and compared. The methods described in this paper are immune to the bad weather conditions including haze, fog, mist and other visibility issues caused by aerosols. Furthermore, the most optimum method is determined for processing RGB images.

Computer vision applications such as Object Detection, Outdoor Surveillance, Object Tracking, Segmentation, consumer electronics and many more require restoration of images captured in foggy environment. Fog/haze is formed as a result of environment attenuation and air light (scattering of light) resulting in image degradation since the contrast of the scene is reduced by attenuation while the whiteness in the scene is increased by airlight. Hence, the objective of fog removal algorithms is to recover the color and contrast of the scene. Also, formation of fog is the function of the depth and estimation of depth information requires assumptions or prior information of the single image. Hence, with various assumptions on the single image, fog removal algorithms estimate the depth information, which are discussed in this paper.

International Journal of Computer Applications, 2014

This paper compares the performance of various filters on the images degraded by the fog. Denoising is vital for the image enhancement. It is difficult to remove the noise from the images while preserving the information and the quality of the image. For analysis filters like Median, Alpha Trim, Lee, Wiener, Anisotropic Diffusion and Guided filter are used. Number of performance metrics exists already in the literature to analyze the performance of denoising filters like SNR (Signal Noise Ratio), MSE (Mean Square Error), NAE (Normalized Absolute Error) and SC (Structural Content).The result demonstrates that the results of filters are not satisfactory. So, recently proposed dark channel prior method is studied and implemented. The visual results of the dark channel method are better than the filters.

International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020

Haze in images is due to natural environmental phenomena, which makes the image in a white shade noise. Haze removal is one of the most important research topics these days to due popularity of applications in real time surveillance from drones or any area under security. Both indoor and outdoor images are important for testing haze and its removal. Many image processing techniques are made by researchers to remove haze in a single image. Haze intensity can be calculated by a parameter known as perceptual fog density (PFD). It is important to analyze this parameter for all the techniques so as to get an idea of improvement. In this paper , a new approach is made by applying globally guided filtering technique with deep neural network. This proposed algorithm is implemented on MATLAB software and results are obtained by calculating the PFD in the existing and proposed technique. The four techniques are compared with each other. The techniques are global filtering (GIF), weighted global filtering(WGIF), Globally guided filtering(GGIF) and proposed technique i.e. Globally guided filtering with DNN (Deep Neural Network). In GIF, the fine structure of the image is generally not preserved and unrealistic image is obtained. In WGIF, the PFD obtained is highest. In GGIF, PFD is lower and Structure is not preserved, but in proposed algorithm, the PDF is minimum with fine structure, color intensity of the picture is of the best quality.

2017

Images are very important parts of dat to day life. They plays very important role in analyzing traffic on roadways, railways and airways.Sometimes due to bad weather effect the analysis through these images becomes difficult. As weather effect degrade the quality of images and those images suffer low contrast, color alteration and shrink the resolution of the captured object in open-air. The reason behind this problem is that the light capture by the lens of the capturing device get spread by the atmosphere. So it was found that conventional techniques used for enhancing the images are not sufficient for removing foggy effect or any other weather effect from the captured images. In this work, we have analyzed the hand techniques employed for image processing. And through that analysis we propose a technique which is efficient technique for enhancing the quality of degraded images. This technique consists of two phases, the first phase is used to remove fog from an image using a Fog...

International Journal of Computer Trends and Technology, 2014

This Research paper involves Image restoration and Image Enhancement technique which will be used for restoring the clear image from a fog degraded image. Image Restoration is an area that deals with improving the appearance of an image. Restoration techniques tend to be based on mathematical or probabilistic models of image degradation. And Image enhancement is an area which deals with improving the quality measure of image. To improve image quality, image enhancement can selectively enhance and restrain some information about image. It is a method which decreases image noise, eliminate artifacts, and maintain details. Its purpose is to amplify certain image features for analysis, diagnosis and display. The overall objective of this paper is to propose an integrated technique which will integrate the nonlinear enhancement technique with the gamma correction and dynamic restoration technique.

International Journal of Image, Graphics and Signal Processing, 2017

Haze and fog lead to image degradation by various degradation processes like image contrast, image blurring and pixel distortion. It has effected the efficiency of computer and machine vision algorithms. A number of single image and multiple image restoration based image defogging algorithms have aimed to solve the problem in an efficient and fast manner. The objective of the paper is to summarize present state of the art image defogging algorithms. Firstly, an image classification algorithm has been presented and then we summarized present state of the art image restoration based image defogging algorithms. Finally, we summarized image quality assessment methods followed by their comparisons of various image defogging algorithms. Problems of image dehazing and future scope have been discussed thereafter.

—Haze is framed because of the two major phenomena that are nature constriction and the air light. This paper introduces an audit on the diverse methods to expel fog from pictures caught in murky environment to recuperate a superior and enhanced nature of murkiness free pictures. Pictures of open air scenes regularly contain corruption because of cloudiness, bringing about difference decrease and shading blurring. Haze evacuation overall called perceivability rebuilding alludes to various frameworks that assume to reduce or empty the corruption that have happened while the computerized picture was being gained. This paper is an audit on the different mist evacuation calculations. Cloudiness evacuation techniques recuperate the shading and differentiation of the scene.In this paper, different haze evacuation methods have been examined.

Fog is the natural phenomenon that causes severe difficulties in driving & results in major accidents. Fog degrades the view of an object and results in poor visibility. The poor visibility of an object becomes challenge to the driver to identify the object and monitor it. It creates lots of difficulties in driving and monitoring the vehicle. There is lots of research on the topic but still the problem has not solved to the desired result. There exist several kinds of environment variations that make the foggy image enhancement more difficult. Therefore an efficient algorithm is required to cope up with several challenges arising from the nature of visibility enhancement of foggy images.

Multimedia Tools and Applications, 2020

Refining visibility through haze removal from image becomes an inevitable chore and essential to recognize and track vehicles, traffic signal, and signs clearly under road safety. That can face a recurrent degradation under destitute climatic circumstances for instance fog, rain, cloud, and smog. To diminish this constraint, various methods were designed and implemented, but most were not capable of obtaining the improved quantitative outcomes. Therefore, a new algorithm Fog Elimination using Multiple Thresholds (FEMT) for single image haze eviction that meritoriously obtains the significant results on both gray and colored over real and synthetic images using multiple thresholds is proposed in this paper. The proposed method targets on the light regions by reducing the brightness and increasing the contrast of image at different levels. Finally, by grouping all the obtained resultant images leads to the generation of the resultant defogged image. The qualitative and quantitative analysis is carried out for an assessment of digitalized de-hazed images acquired from the proposed algorithm and compared to the prior techniques. Simulated fallouts entitle high resemblance to the corresponding ground truth, reduction in computation time consumption to 88% and error of 98%. The proposed approach can be applied in the field of robotics, human activity monitoring, smart systems, and digital investigation on the hazy images.