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Found 5 papers, 0 papers with code
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.
Deep Pneumonia: Attention-Based Contrastive Learning for Class-Imbalanced Pneumonia Lesion Recognition in Chest X-rays
Computer-aided X-ray pneumonia lesion recognition is important for accurate diagnosis of pneumonia.
TSN-CA: A Two-Stage Network with Channel Attention for Low-Light Image Enhancement
In this paper, we propose a Two-Stage Network with Channel Attention (denoted as TSN-CA) to enhance the brightness of the low-light image and restore the enhanced images from various kinds of degradation.
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).
