Single Image Dehazing Using Ranking Convolutional Neural Network

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. Analysing existing approaches, the common key step is to estimate the haze density of each pixel. To this end, various approaches often heuristically designed haze-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 be automatically learned from 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.

Image Dehazing Based on (CMTnet) Cascaded Multi-scale Convolutional Neural Networks and Efficient Light Estimation Algorithm

“…This proposed dehazing model employs hazy images as an input and builds their transmission maps, and then restores clear images by using the atmospheric scattering model. A ranking convolutional neural network (Ranking-CNN) [29] has been presented to recover clear images from hazy ones. The proposed architecture employs conventional CNN with the insertion of a new ranking layer to well-capture both the statistical and structural characteristics of the hazy image together.…”

“…Despite the impressive achievement of learning-based dehazing techniques, they still have some problems that appear in results, in terms of saturation, and naturalness of recovered hazy image because of non-massive data in the learning operation. In addition, redundant computations increase the computational complexity, as mentioned in Song et al's [29] conclusion. Additionally, most of the existing CNN-based dehazing approaches estimate only the transmission medium of hazy images and neglect the atmospheric light estimation step.…”

“…At present, Convolutional Neural Networks (CNNs) have attained great success in addressing several low-level computer vision and image processing tasks, e.g., image segmentation [21][22][23], object detection [24], and image denoising [25]. Many researchers [26][27][28][29] have applied Convolutional Neural Networks (CNNs) to explore haze-relevant features deeply. Ren et al and Rashid et al [26,27] proposed multi-scale CNN frameworks to calculate the transmission medium from the input hazy image directly.…”

“…Cai et al [28] presented an End-to-End framework based on CNNs estimating the transmission of image pixels within small patches. To make the learning process easier, Song et al [29] added a new ranking layer into the classical CNN framework, which can capture the structural and statistical attributes jointly.…”

Adaptive Multi-Scale Entropy Fusion De-Hazing Based on Fractional Order

“…Other recent algorithms utilize hybrid methods, deep learning, and convolutional neural network architectures for de-hazing and include works by References [55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73].…”