75 research outputs found
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
Non-aligned supervision for Real Image Dehazing
Removing haze from real-world images is challenging due to unpredictable
weather conditions, resulting in misaligned hazy and clear image pairs. In this
paper, we propose a non-aligned supervision framework that consists of three
networks - dehazing, airlight, and transmission. In particular, we explore a
non-alignment setting by utilizing a clear reference image that is not aligned
with the hazy input image to supervise the dehazing network through a
multi-scale reference loss that compares the features of the two images. Our
setting makes it easier to collect hazy/clear image pairs in real-world
environments, even under conditions of misalignment and shift views. To
demonstrate this, we have created a new hazy dataset called "Phone-Hazy", which
was captured using mobile phones in both rural and urban areas. Additionally,
we present a mean and variance self-attention network to model the infinite
airlight using dark channel prior as position guidance, and employ a channel
attention network to estimate the three-channel transmission. Experimental
results show that our framework outperforms current state-of-the-art methods in
the real-world image dehazing. Phone-Hazy and code will be available at
https://github.com/hello2377/NSDNet
Initial Results in Underwater Single Image Dehazing
As light is transmitted from subject to observer it is absorbed and scattered by the medium it passes through. In mediums with large suspended particles, such as fog or turbid water, the effect of scattering can drastically decrease the quality of images. In this paper we present an algorithm for removing the effects of light scattering, referred to as dehazing, in underwater images. Our key contribution is to propose a simple, yet effective, prior that exploits the strong difference in attenuation between the three image color channels in water to estimate the depth of the scene. We then use this estimate to reduce the spatially varying effect of haze in the image. Our method works with a single image and does not require any specialized hardware or prior knowledge of the scene. As a by-product of the dehazing process, an up-to-scale depth map of the scene is produced. We present results over multiple real underwater images and over a controlled test set where the target distance and true colors are known.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86035/1/ncarlevaris-3.pd
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