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Found 10 papers, 9 papers with code
Towards Real-time Video Compressive Sensing on Mobile Devices
The fast evolving mobile devices and existing high-performance video SCI reconstruction algorithms motivate us to develop mobile reconstruction methods for real-world applications.
A Simple Low-bit Quantization Framework for Video Snapshot Compressive Imaging
To address this challenge, in this paper, we propose a simple low-bit quantization framework (dubbed Q-SCI) for the end-to-end deep learning-based video SCI reconstruction methods which usually consist of a feature extraction, feature enhancement, and video reconstruction module.
Hierarchical Separable Video Transformer for Snapshot Compressive Imaging
Transformers have achieved the state-of-the-art performance on solving the inverse problem of Snapshot Compressive Imaging (SCI) for video, whose ill-posedness is rooted in the mixed degradation of spatial masking and temporal aliasing.
Coarse-Fine Spectral-Aware Deformable Convolution For Hyperspectral Image Reconstruction
Considering the sparsity of HSI, we design a deformable convolution module that exploits its deformability to capture long-range dependencies and non-local similarities.
Hybrid CNN-Transformer Architecture for Efficient Large-Scale Video Snapshot Compressive Imaging
We are the first time to demonstrate that a UHD color video (1644×3840 ×3) with high compression ratio (40) can be reconstructed from a snapshot 2D measurement using a single end-to-end deep learning model with PSNR above 34 dB.
Deep Optics for Video Snapshot Compressive Imaging
We believe this is a milestone for real-world video SCI.
WaveNet: Wave-Aware Image Enhancement
In this paper, we formulate the enhancement into a signal modulation problem and propose the WaveNet architecture, which performs well in various parameters and improves the feature expression using wave-like feature representation.
Ranked #7 on
Low-Light Image Enhancement
on LOL
EfficientSCI: Densely Connected Network with Space-time Factorization for Large-scale Video Snapshot Compressive Imaging
We are the first time to show that an UHD color video with high compression ratio can be reconstructed from a snapshot 2D measurement using a single end-to-end deep learning model with PSNR above 32 dB.
Spatial-Temporal Transformer for Video Snapshot Compressive Imaging
In this paper, we consider the reconstruction algorithm in video SCI, i. e., recovering a series of video frames from a compressed measurement.
Spectral Compressive Imaging Reconstruction Using Convolution and Contextual Transformer
Spectral compressive imaging (SCI) is able to encode the high-dimensional hyperspectral image to a 2D measurement, and then uses algorithms to reconstruct the spatio-spectral data-cube.
