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Found 4 papers, 1 papers with code
Transformer-based Learned Image Compression for Joint Decoding and Denoising
Instead of training separate decoders for these tasks, we incorporate two add-on modules to adapt a pre-trained image decoder from performing the standard image reconstruction to joint decoding and denoising.
LiDAR Depth Map Guided Image Compression Model
In this paper, we introduce a novel direction that harnesses LiDAR depth maps to enhance the compression of the corresponding RGB camera images.
BS-Net: learning COVID-19 pneumonia severity on a large Chest X-Ray dataset
In this work we design an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients.
Feature Fusion for Robust Patch Matching With Compact Binary Descriptors
We propose a convolutional network framework for learning binary patch descriptors where pixel domain features are fused with features extracted from the transformed domain.
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