2,508 research outputs found
Learning of Image Dehazing Models for Segmentation Tasks
To evaluate their performance, existing dehazing approaches generally rely on
distance measures between the generated image and its corresponding ground
truth. Despite its ability to produce visually good images, using pixel-based
or even perceptual metrics do not guarantee, in general, that the produced
image is fit for being used as input for low-level computer vision tasks such
as segmentation. To overcome this weakness, we are proposing a novel end-to-end
approach for image dehazing, fit for being used as input to an image
segmentation procedure, while maintaining the visual quality of the generated
images. Inspired by the success of Generative Adversarial Networks (GAN), we
propose to optimize the generator by introducing a discriminator network and a
loss function that evaluates segmentation quality of dehazed images. In
addition, we make use of a supplementary loss function that verifies that the
visual and the perceptual quality of the generated image are preserved in hazy
conditions. Results obtained using the proposed technique are appealing, with a
favorable comparison to state-of-the-art approaches when considering the
performance of segmentation algorithms on the hazy images.Comment: Accepted in EUSIPCO 201
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
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