Tensorf: Tensorial radiance fields
We present TensoRF, a novel approach to model and reconstruct radiance fields. Unlike
NeRF that purely uses MLPs, we model the radiance field of a scene as a 4D tensor, which …
NeRF that purely uses MLPs, we model the radiance field of a scene as a 4D tensor, which …
Towards robust pattern recognition: A review
The accuracies for many pattern recognition tasks have increased rapidly year by year,
achieving or even outperforming human performance. From the perspective of accuracy …
achieving or even outperforming human performance. From the perspective of accuracy …
ADMM-CSNet: A deep learning approach for image compressive sensing
Compressive sensing (CS) is an effective technique for reconstructing image from a small
amount of sampled data. It has been widely applied in medical imaging, remote sensing …
amount of sampled data. It has been widely applied in medical imaging, remote sensing …
Weighted nuclear norm minimization and its applications to low level vision
As a convex relaxation of the rank minimization model, the nuclear norm minimization
(NNM) problem has been attracting significant research interest in recent years. The …
(NNM) problem has been attracting significant research interest in recent years. The …
Reconnet: Non-iterative reconstruction of images from compressively sensed measurements
The goal of this paper is to present a non-iterative and more importantly an extremely fast
algorithm to reconstruct images from compressively sensed (CS) random measurements. To …
algorithm to reconstruct images from compressively sensed (CS) random measurements. To …
Spectral enhanced rectangle transformer for hyperspectral image denoising
Denoising is a crucial step for hyperspectral image (HSI) applications. Though witnessing
the great power of deep learning, existing HSI denoising methods suffer from limitations in …
the great power of deep learning, existing HSI denoising methods suffer from limitations in …
Rank minimization for snapshot compressive imaging
Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple
frames are mapped into a single measurement, with video compressive imaging and …
frames are mapped into a single measurement, with video compressive imaging and …
Non-local meets global: An iterative paradigm for hyperspectral image restoration
Non-local low-rank tensor approximation has been developed as a state-of-the-art method
for hyperspectral image (HSI) restoration, which includes the tasks of denoising …
for hyperspectral image (HSI) restoration, which includes the tasks of denoising …