Search Results for author:
Found 11 papers, 8 papers with code
Single-shot Phase Retrieval from a Fractional Fourier Transform Perspective
The realm of classical phase retrieval concerns itself with the arduous task of recovering a signal from its Fourier magnitude measurements, which are fraught with inherent ambiguities.
Spatially Varying Nanophotonic Neural Networks
The explosive growth of computation and energy cost of artificial intelligence has spurred strong interests in new computing modalities as potential alternatives to conventional electronic processors.
Instance Segmentation in the Dark
Existing instance segmentation techniques are primarily tailored for high-visibility inputs, but their performance significantly deteriorates in extremely low-light environments.
Deep Plug-and-Play Prior for Hyperspectral Image Restoration
Deep-learning-based hyperspectral image (HSI) restoration methods have gained great popularity for their remarkable performance but often demand expensive network retraining whenever the specifics of task changes.
Physics-based Noise Modeling for Extreme Low-light Photography
Enhancing the visibility in extreme low-light environments is a challenging task.
Ranked #4 on
Image Denoising
on SID SonyA7S2 x100
Dynamic Proximal Unrolling Network for Compressive Imaging
In this paper, a dynamic proximal unrolling network (dubbed DPUNet) was proposed, which can handle a variety of measurement matrices via one single model without retraining.
TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems
In this work, we present a class of tuning-free PnP proximal algorithms that can determine parameters such as denoising strength, termination time, and other optimization-specific parameters automatically.
A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising
Lacking rich and realistic data, learned single image denoising algorithms generalize poorly to real raw images that do not resemble the data used for training.
Ranked #5 on
Image Denoising
on ELD SonyA7S2 x200
3D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising
In this paper, we propose an alternating directional 3D quasi-recurrent neural network for hyperspectral image (HSI) denoising, which can effectively embed the domain knowledge -- structural spatio-spectral correlation and global correlation along spectrum.
Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems
Moreover, we discuss the practical considerations of the plugged denoisers, which together with our learned policy yield state-of-the-art results.
Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements
Removing undesirable reflections from a single image captured through a glass window is of practical importance to visual computing systems.
Ranked #2 on
Reflection Removal
on SIR^2(Objects)
