{"title":"Foggy Image Detection and Filtration Methods: Review","authors":"Priyanka, Geetanjali Babbar","doi":"10.46860/CGCIJCTR.2021.06.31.211","DOIUrl":null,"url":null,"abstract":"Image de-fogging in brightness defined to image calculated in a deprived climate like as fog, rain and ocean and pollutants or dust particles. To alter the fog and some other pollutants from the image, various methods are customized, some mainly utilized methods are DCP, Detection, and Classification of foggy images. Haze is an arrangement of dual components, air-light and DA (Direct Attenuation), low image quality and generates various issues in VS (Video Surveillance), Navigation and Target Tracking, etc. So, its removes from an image, several de-fogging approaches have been discussed in this paper. Image De-fogging can attain utilizing several and single image haze removal techniques. The famous methods are discussed in this paper used for image de-fogging in DCP, Depth-map for accurate estimation, Guided Filter, and Transmission methods. These techniques still efficient in removing haze from images have very high time complexity. The guided filter is a new region preservative filter with region enhancement and smoothing. The previous result was a local linear transformation of the Guided Image. It defines a review of the classification and detection technique of a hazy image. This method mitigates the limitations of filtration and DCP and at the same time preserves the image quality. At that time, described the existing image de-fogging methods containing image restoration, contrast improvement, and fusion-based image de-fogging methods.","PeriodicalId":373538,"journal":{"name":"CGC International Journal of Contemporary Technology and Research","volume":"100 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"CGC International Journal of Contemporary Technology and Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46860/CGCIJCTR.2021.06.31.211","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Image de-fogging in brightness defined to image calculated in a deprived climate like as fog, rain and ocean and pollutants or dust particles. To alter the fog and some other pollutants from the image, various methods are customized, some mainly utilized methods are DCP, Detection, and Classification of foggy images. Haze is an arrangement of dual components, air-light and DA (Direct Attenuation), low image quality and generates various issues in VS (Video Surveillance), Navigation and Target Tracking, etc. So, its removes from an image, several de-fogging approaches have been discussed in this paper. Image De-fogging can attain utilizing several and single image haze removal techniques. The famous methods are discussed in this paper used for image de-fogging in DCP, Depth-map for accurate estimation, Guided Filter, and Transmission methods. These techniques still efficient in removing haze from images have very high time complexity. The guided filter is a new region preservative filter with region enhancement and smoothing. The previous result was a local linear transformation of the Guided Image. It defines a review of the classification and detection technique of a hazy image. This method mitigates the limitations of filtration and DCP and at the same time preserves the image quality. At that time, described the existing image de-fogging methods containing image restoration, contrast improvement, and fusion-based image de-fogging methods.
图像去雾的亮度定义为在雾、雨、海洋、污染物或尘埃颗粒等恶劣气候条件下计算的图像。为了改变图像中的雾和其他一些污染物,我们定制了各种方法,主要使用的方法有DCP、Detection和Classification of fog images。雾霾是空气光和DA(直接衰减)双组分的排列,图像质量较低,在VS(视频监控)、导航和目标跟踪等方面产生各种问题。因此,本文讨论了从图像中去除雾的几种方法。图像除雾可以实现利用几种和单一的图像雾霾去除技术。本文讨论了DCP中常用的图像除雾方法、精确估计的深度图方法、引导滤波方法和传输方法。这些技术仍然有效地从图像中去除雾霾,但时间复杂度非常高。导频滤波器是一种具有区域增强和平滑特性的新型区域保存滤波器。之前的结果是对引导图像进行局部线性变换。综述了模糊图像的分类与检测技术。该方法在保证图像质量的同时,减轻了滤波和DCP的局限性。当时,描述了现有的图像除雾方法,包括图像恢复、对比度改善和基于融合的图像除雾方法。