On the use of deep learning for computational imaging
Since their inception in the 1930–1960s, the research disciplines of computational imaging
and machine learning have followed parallel tracks and, during the last two decades …
and machine learning have followed parallel tracks and, during the last two decades …
Learning to see in the dark
Imaging in low light is challenging due to low photon count and low SNR. Short-exposure
images suffer from noise, while long exposure can lead to blurry images and is often …
images suffer from noise, while long exposure can lead to blurry images and is often …
Image de-noising with machine learning: A review
Images are susceptible to various kinds of noises, which corrupt the pictorial information
stored in the images. Image de-noising has become an integral part of the image processing …
stored in the images. Image de-noising has become an integral part of the image processing …
Attention guided low-light image enhancement with a large scale low-light simulation dataset
Low-light image enhancement is challenging in that it needs to consider not only brightness
recovery but also complex issues like color distortion and noise, which usually hide in the …
recovery but also complex issues like color distortion and noise, which usually hide in the …
Imaging through glass diffusers using densely connected convolutional networks
Computational imaging through scatter generally is accomplished by first characterizing the
scattering medium so that its forward operator is obtained and then imposing additional …
scattering medium so that its forward operator is obtained and then imposing additional …
Getting to know low-light images with the exclusively dark dataset
Low-light is an inescapable element of our daily surroundings that greatly affects the
efficiency of our vision. Research works on low-light imagery have seen a steady growth …
efficiency of our vision. Research works on low-light imagery have seen a steady growth …
Dynamic low-light imaging with quanta image sensors
Imaging in low light is difficult because the number of photons arriving at the sensor is low.
Imaging dynamic scenes in low-light environments is even more difficult because as the …
Imaging dynamic scenes in low-light environments is even more difficult because as the …
Review of quanta image sensors for ultralow-light imaging
The quanta image sensor (QIS) is a photon-counting image sensor that has been
implemented using different electron devices, including impact ionization-gain devices, such …
implemented using different electron devices, including impact ionization-gain devices, such …
Extreme low-light environment-driven image denoising over permanently shadowed lunar regions with a physical noise model
Recently, learning-based approaches have achieved impressive results in the field of low-
light image denoising. Some state of the art approaches employ a rich physical model to …
light image denoising. Some state of the art approaches employ a rich physical model to …
Dn-resnet: Efficient deep residual network for image denoising
A deep learning approach to blind denoising of images without complete knowledge of the
noise statistics is considered. We propose DN-ResNet, which is a deep convolutional neural …
noise statistics is considered. We propose DN-ResNet, which is a deep convolutional neural …