599 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
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding
This work addresses the problem of semantic scene understanding under dense
fog. Although considerable progress has been made in semantic scene
understanding, it is mainly related to clear-weather scenes. Extending
recognition methods to adverse weather conditions such as fog is crucial for
outdoor applications. In this paper, we propose a novel method, named
Curriculum Model Adaptation (CMAda), which gradually adapts a semantic
segmentation model from light synthetic fog to dense real fog in multiple
steps, using both synthetic and real foggy data. In addition, we present three
other main stand-alone contributions: 1) a novel method to add synthetic fog to
real, clear-weather scenes using semantic input; 2) a new fog density
estimator; 3) the Foggy Zurich dataset comprising real foggy images,
with pixel-level semantic annotations for images with dense fog. Our
experiments show that 1) our fog simulation slightly outperforms a
state-of-the-art competing simulation with respect to the task of semantic
foggy scene understanding (SFSU); 2) CMAda improves the performance of
state-of-the-art models for SFSU significantly by leveraging unlabeled real
foggy data. The datasets and code are publicly available.Comment: final version, ECCV 201
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