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Found 8 papers, 4 papers with code
Designed Dithering Sign Activation for Binary Neural Networks
Binary Neural Networks emerged as a cost-effective and energy-efficient solution for computer vision tasks by binarizing either network weights or activations.
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery
This paper proposes a non-data-driven deep neural network for spectral image recovery problems such as denoising, single hyperspectral image super-resolution, and compressive spectral imaging reconstruction.
Deep Optical Coding Design in Computational Imaging
The performance of COI systems highly depends on the design of its main components: the CE pattern and the computational method used to perform a given task.
Deep Coding Patterns Design for Compressive Near-Infrared Spectral Classification
Compressive spectral imaging (CSI) has emerged as an attractive compression and sensing technique, primarily to sense spectral regions where traditional systems result in highly costly such as in the near-infrared spectrum.
D$^\text{2}$UF: Deep Coded Aperture Design and Unrolling Algorithm for Compressive Spectral Image Fusion
To overcome this issue, compressive spectral image fusion (CSIF) employs the projected measurements of two CSI architectures with different resolutions to estimate a high-spatial high-spectral resolution.
JR2net: A Joint Non-Linear Representation and Recovery Network for Compressive Spectral Imaging
Deep learning models are state-of-the-art in compressive spectral imaging (CSI) recovery.
Deep Low-Dimensional Spectral Image Representation for Compressive Spectral Reconstruction
This paper proposes an autoencoder-based network that guarantees a low-dimensional spectral representation through feature reduction, which can be used as prior in the compressive spectral imaging reconstruction.
Compressive Spectral Image Reconstruction using Deep Prior and Low-Rank Tensor Representation
The proposed method is based on the fact that the structure of some deep neural networks and an appropriated low-dimensional structure are sufficient to impose a structure of the underlying spectral image from CSI.
