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Found 4 papers, 1 papers with code
Dual camera snapshot hyperspectral imaging system via physics informed learning
Various methods using CNNs have been developed in recent years to reconstruct HSIs, but most of the supervised deep learning methods aimed to fit a brute-force mapping relationship between the captured compressed image and standard HSIs.
Learning-based real-time method to looking through scattering medium beyond the memory effect
Strong scattering medium brings great difficulties to optical imaging, which is also a problem in medical imaging and many other fields.
High Sensitivity Snapshot Spectrometer Based on Deep Network Unmixing
In this paper, we present a convolution neural network based method to recover the light intensity distribution from the overlapped dispersive spectra instead of adding an extra light path to capture it directly for the first time.
Residual Pyramid Learning for Single-Shot Semantic Segmentation
In this paper, we put forward a method for single-shot segmentation in a feature residual pyramid network (RPNet), which learns the main and residuals of segmentation by decomposing the label at different levels of residual blocks.
