66 research outputs found
Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression
Night images suffer not only from low light, but also from uneven
distributions of light. Most existing night visibility enhancement methods
focus mainly on enhancing low-light regions. This inevitably leads to over
enhancement and saturation in bright regions, such as those regions affected by
light effects (glare, floodlight, etc). To address this problem, we need to
suppress the light effects in bright regions while, at the same time, boosting
the intensity of dark regions. With this idea in mind, we introduce an
unsupervised method that integrates a layer decomposition network and a
light-effects suppression network. Given a single night image as input, our
decomposition network learns to decompose shading, reflectance and
light-effects layers, guided by unsupervised layer-specific prior losses. Our
light-effects suppression network further suppresses the light effects and, at
the same time, enhances the illumination in dark regions. This light-effects
suppression network exploits the estimated light-effects layer as the guidance
to focus on the light-effects regions. To recover the background details and
reduce hallucination/artefacts, we propose structure and high-frequency
consistency losses. Our quantitative and qualitative evaluations on real images
show that our method outperforms state-of-the-art methods in suppressing night
light effects and boosting the intensity of dark regions.Comment: Accepted to ECCV202
Estimating Reflectance Layer from A Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning
Estimating reflectance layer from a single image is a challenging task. It
becomes more challenging when the input image contains shadows or specular
highlights, which often render an inaccurate estimate of the reflectance layer.
Therefore, we propose a two-stage learning method, including reflectance
guidance and a Shadow/Specular-Aware (S-Aware) network to tackle the problem.
In the first stage, an initial reflectance layer free from shadows and
specularities is obtained with the constraint of novel losses that are guided
by prior-based shadow-free and specular-free images. To further enforce the
reflectance layer to be independent from shadows and specularities in the
second-stage refinement, we introduce an S-Aware network that distinguishes the
reflectance image from the input image. Our network employs a classifier to
categorize shadow/shadow-free, specular/specular-free classes, enabling the
activation features to function as attention maps that focus on shadow/specular
regions. Our quantitative and qualitative evaluations show that our method
outperforms the state-of-the-art methods in the reflectance layer estimation
that is free from shadows and specularities.Comment: Accepted to AAAI202
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