Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …

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 …

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 …

Signal processing, communications, and control have traditionally relied on classical
statistical modeling techniques. Such model-based methods utilize mathematical …

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 …

Computational imaging through scatter generally is accomplished by first characterizing the
scattering medium so that its forward operator is obtained and then imposing additional …

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 …

We investigate the task of learning blind image denoising networks from an unpaired set of
clean and noisy images. Such problem setting generally is practical and valuable …

Image denoising—removal of additive white Gaussian noise from an image—is one of the
oldest and most studied problems in image processing. Extensive work over several …

In recent years, supervised-deep-learning methods have shown some advantages over
conventional methods in seismic data denoising, such as higher signal-to-noise ratio after …