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Found 9 papers, 7 papers with code
Auto-Classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model
Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists.
Synthesizing New Retinal Symptom Images by Multiple Generative Models
Age-Related Macular Degeneration (AMD) is an asymptomatic retinal disease which may result in loss of vision.
Y-net: Multi-scale feature aggregation network with wavelet structure similarity loss function for single image dehazing
Extensive experimental results demonstrate that the proposed Y-net with the W-SSIM loss function restores high-quality clear images and outperforms state-of-the-art algorithms.
Wavelet Channel Attention Module with a Fusion Network for Single Image Deraining
Wavelet transform and the inverse wavelet transform are substituted for down-sampling and up-sampling so feature maps from the wavelet transform and convolutions contain different frequencies and scales.
Multi-modal Bifurcated Network for Depth Guided Image Relighting
This model extracts the image and the depth features by the bifurcated network in the encoder.
S3Net: A Single Stream Structure for Depth Guided Image Relighting
Depth guided any-to-any image relighting aims to generate a relit image from the original image and corresponding depth maps to match the illumination setting of the given guided image and its depth map.
LAFFNet: A Lightweight Adaptive Feature Fusion Network for Underwater Image Enhancement
The model is the encoder-decoder model with multiple adaptive feature fusion (AAF) modules.
Learning Multiple Adverse Weather Removal via Two-Stage Knowledge Learning and Multi-Contrastive Regularization: Toward a Unified Model
At the KC, the student network aims to learn the comprehensive bad weather removal problem from multiple well-trained teacher networks where each of them is specialized in a specific bad weather removal problem.
RVSL: Robust Vehicle Similarity Learning in Real Hazy Scenes Based on Semi-supervised Learning
Recently, vehicle similarity learning, also called re-identification (ReID), has attracted significant attention in computer vision.
