Year: 2017
Type: article
Abstract: Single image dehazing, which aims to recover the clear image solely from an input hazy or foggy image, is a challenging ill-posed problem. Analyzing existing approaches, the common key step is to estimate the haze density of each pixel. To this end, various approaches oftenheuristically designedhaze-relevant features. Several recent works also automatically learn the features via directly exploiting convolutional neural networks (CNN). However, it may be insufficient to fully capture the intrinsic attributes of hazy images. To obtain effective features for single image dehazing, this paper presents a novel ranking convolutional neural network (Ranking-CNN). In Ranking-CNN, a novel ranking layer is proposed to extend the structure of CNN so that the statistical and structural attributes of hazy images can be simultaneously captured. By training Ranking-CNN in a well-designed manner, powerful haze-relevant features can beautomatically learnedfrom massive hazy image patches. Based on these features, haze can be effectively removed by using a haze density prediction model trained through the random forest regression. Experimental results show that our approach outperforms several previous dehazing approaches on synthetic and real-world benchmark images. Comprehensive analyses are also conducted to interpret the proposed Ranking-CNN from both the theoretical and experimental aspects.
Source: IEEE Transactions on Multimedia
Authors: Yafei Song, Jia Li, Xiaogang Wang, Xiaowu Chen
Institution: Beihang University
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2767516232