I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images

Image dehazing has become an important computational imaging topic in the recent years. However, due to the lack of ground truth images, the comparison of dehazing methods is not straightforward, nor objective. To overcome this issue we introduce a new dataset -named I-HAZE- that contains 35 image pairs of hazy and corresponding haze-free (ground-truth) indoor images. Different from most of the existing dehazing databases, hazy images have been generated using real haze produced by a professional haze machine. For easy color calibration and improved assessment of dehazing algorithms, each scene include a MacBeth color checker. Moreover, since the images are captured in a controlled environment, both haze-free and hazy images are captured under the same illumination conditions. This represents an important advantage of the I-HAZE dataset that allows us to objectively compare the existing image dehazing techniques using traditional image quality metrics such as PSNR and SSIM

Blur-Robust Face Recognition via Transformation Learning

Abstract. This paper introduces a new method for recognizing faces degraded by blur using transformation learning on the image feature. The basic idea is to transform both the sharp images and blurred im-ages to a same feature subspace by the method of multidimensional s-caling. Different from the method of finding blur-invariant descriptors, our method learns the transformation which both preserves the mani-fold structure of the original shape images and, at the same time, en-hances the class separability, resulting in a wide applications to various descriptors. Furthermore, we combine our method with subspace-based point spread function (PSF) estimation method to handle cases of un-known blur degree, by applying the feature transformation correspond-ing to the best matched PSF, where the transformation for each PSF is learned in the training stage. Experimental results on the FERET database show the proposed method achieve comparable performance a-gainst the state-of-the-art blur-invariant face recognition methods, such as LPQ and FADEIN.

Mt. Kelud haze removal using color attenuation prior

Kelud crater observation using closed-circuit television (CCTV) has not been used as the main guide in the world of volcanology. This is caused by observations manually by volcanologist who is not certain and depends on their ability and experience. In practice, there is still obstacles haze in the image taken from CCTV record. This paper present color attenuation prior method to eliminate haze on the digital image. The results obtained showed that the selected method is capable of eliminating sparse haze and moderate haze but not dense haze

Physically Plausible Dehazing for Non-physical Dehazing Algorithms

Images affected by haze usually present faded colours and loss of contrast, hindering the precision of methods devised for clear images. For this reason, image dehazing is a crucial pre-processing step for applications such as self-driving vehicles or tracking. Some of the most successful dehazing methods in the literature do not follow any physical model and are just based on either image enhancement or image fusion. In this paper, we present a procedure to allow these methods to accomplish the Koschmieder physical model, i.e., to force them to have a unique transmission for all the channels, instead of the per-channel transmission they obtain. Our method is based on coupling the results obtained for each of the three colour channels. It improves the results of the original methods both quantitatively using image metrics, and subjectively via a psychophysical test. It especially helps in terms of avoiding over-saturation and reducing colour artefacts, which are the most common complications faced by image dehazing methods

Single image dehazing for a variety of haze scenarios using back projected pyramid network

Learning to dehaze single hazy images, especially using a small training dataset is quite challenging. We propose a novel generative adversarial network architecture for this problem, namely back projected pyramid network (BPPNet), that gives good performance for a variety of challenging haze conditions, including dense haze and inhomogeneous haze. Our architecture incorporates learning of multiple levels of complexities while retaining spatial context through iterative blocks of UNets and structural information of multiple scales through a novel pyramidal convolution block. These blocks together for the generator and are amenable to learning through back projection. We have shown that our network can be trained without over-fitting using as few as 20 image pairs of hazy and non-hazy images. We report the state of the art performances on NTIRE 2018 homogeneous haze datasets for indoor and outdoor images, NTIRE 2019 denseHaze dataset, and NTIRE 2020 non-homogeneous haze dataset