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Orientation and Context Entangled Network for Retinal Vessel Segmentation
In this paper, we propose a robust Orientation and Context Entangled Network (denoted as OCE-Net), which has the capability of extracting complex orientation and context information of the blood vessels.
DA-DRN: Degradation-Aware Deep Retinex Network for Low-Light Image Enhancement
We propose a Degradation-Aware Module (DA Module) which can guide the training process of the decomposer and enable the decomposer to be a restorer during the training phase without additional computational cost in the test phase.
BLNet: A Fast Deep Learning Framework for Low-Light Image Enhancement with Noise Removal and Color Restoration
We propose a Noise and Color Bias Control module (NCBC Module) that contains a convolutional neural network and two loss functions (noise loss and color loss).
Efficient Color Boundary Detection with Color-Opponent Mechanisms
Color information plays an important role in better understanding of natural scenes by at least facilitating discriminating boundaries of objects or areas.
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