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Found 6 papers, 2 papers with code
DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised Domain-Classifier Guided Network
To address the problem, in this paper, we propose an unsupervised domain-classifier guided shadow removal network, DC-ShadowNet.
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Shadow Removal
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Nighttime Visibility Enhancement by Increasing the Dynamic Range and Suppression of Light Effects
In this paper, given a single nighttime image as input, our goal is to enhance its visibility by increasing the dynamic range of the intensity, and thus can boost the intensity of the low light regions, and at the same time, suppress the light effects (glow, glare) simultaneously.
Estimation of Camera Response Function using Prediction Consistency and Gradual Refinement with an Extension to Deep Learning
Second, we employ a gradual refinement scheme in which we start from a simple CRF model to generate a result which is more robust to noise but less accurate, and then we gradually increase the model's complexity to improve the result.
Optical Flow in Dense Foggy Scenes using Semi-Supervised Learning
Initially, given a pair of synthetic fog images, its corresponding clean images and optical flow ground-truths, in one training batch we train our network in a supervised manner.
Nighttime Stereo Depth Estimation using Joint Translation-Stereo Learning: Light Effects and Uninformative Regions
To address the problem, we introduce a network joining day/night translation and stereo.
Into the Twilight Zone: Depth Estimation using Joint Structure-Stereo Optimization
We present a joint Structure-Stereo optimization model that is robust for disparity estimation under low-light conditions.
