You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network

Abstract

In this paper, we study two challenging and less-touched problems in single image dehazing, namely, how to make deep learning achieve image dehazing without training on the ground-truth clean image (unsupervised) and an image collection (untrained). An unsupervised model will avoid the intensive labor of collecting hazy-clean image pairs, and an untrained model is a "real" single image dehazing approach which could remove haze based on the observed hazy image only and no extra images are used. Motivated by the layer disentanglement, we propose a novel method, called you only look yourself (YOLY) which could be one of the first unsupervised and untrained neural networks for image dehazing. In brief, YOLY employs three joint subnetworks to separate the observed hazy image into several latent layers, i.e., scene radiance layer, transmission map layer, and atmospheric light layer. After that, three layers are further composed to the hazy image in a selfsupervised manner. Thanks to the unsupervised and untrained characteristics of YOLY, our method bypasses the conventional training paradigm of deep models on hazy-clean pairs or a large scale dataset, thus avoids the labor-intensive data collection and the domain shift issue. Besides, our method also provides an effective learning-based haze transfer solution thanks to its layer disentanglement mechanism. Extensive experiments show the promising performance of our method in image dehazing compared with 14 methods on six databases. The code could be accessed at www.pengxi.me. Keywords Single image dehazing • Unsupervised learning • Untrained neural network Communicated by Vishal Patel.

Unsupervised water scene dehazing network using multiple scattering model

“…Since the above shortcomings of learning-based image enhancement methods, unsupervised image enhancement methods have been proposed [5,[21][22][23]. For instance, in response to the problems with data-driven learning methods, Ulyanov et al [22] proposed a deep image prior method (DIP).…”

“…Although this method can solve complex image enhancement problems through multiple DIPs, it still uses the early stopping strategy when fits the network model, which will not be conducive to the estimation of the optimal result. Li et al [5] proposed a new unsupervised image dehazing method for the problem with the early stopping strategy. They inputted the hazy image into the unsupervised network and constrained unsupervised network by minimizing the loss of input hazy image and output synthetized hazy image.…”

“…Although this method can achieve unsupervised dehazing, the final output is still sub-optimal because of using the atmospheric single scattering model. This method is hereafter referred to as YOLY [5].…”

“…In these conditions, the frequent occurrence of haze often causes the captured images to lose detailed information, and affects lots of visual tasks in water conditions, such as target detection and object classification. So far, although many methods of image dehazing have been proposed and achieved desired effects [1][2][3][4][5][6], the dehazing algorithms for water conditions have not been paid much attention to. Unlike land conditions, there is a lot of dense haze in water conditions and it is difficult to collect ideal data sets.…”

“…But the unsupervised image enhancement method only focuses on to the information of the input image itself and does not require a large number of training sets to train the network model, it has attracted widespread attention from researchers in recent years. At the same time, a variety of image enhancement methods based on unsupervised learning have been proposed [5,[21][22][23]. In summary, in the prior-based image dehazing methods, there are too much reliance on the prior knowledge, resulting in suboptimal dehazing in certain changing scenes.…”