The advent of deep learning has brought a revolutionary transformation to image denoising
techniques. However, the persistent challenge of acquiring noise-clean pairs for supervised …

Collaborative filters perform denoising through transform-domain shrinkage of a group of
similar patches extracted from an image. Existing collaborative filters of stationary correlated …

We present a method for training a neural network to perform image denoising without
access to clean training examples or access to paired noisy training examples. Our method …

Recovering a high-quality image from noisy indirect measurements is an important problem
with many applications. For such inverse problems, supervised deep convolutional neural …

The lack of large-scale real raw image denoising dataset gives the rise to challenges on
synthesizing realistic raw image noise for training denoising models. However, the real raw …

The lack of large-scale noisy-clean image pairs restricts supervised denoising methods'
deployment in actual applications. While existing unsupervised methods are able to learn …

Hyperspectral imaging offers new perspectives for diverse applications, ranging from the
monitoring of the environment using airborne or satellite remote sensing, precision farming …

Color image restoration is one of the basic tasks in pattern recognition. Unlike grayscale
image, each color image has three channels in the RGB color space. Due to the inner …

Fiber-optic distributed acoustic sensing (DAS) is an emerging technology for vibration
measurements with numerous applications in seismic signal analysis, including …

Uncovering the architecture of white matter axons is fundamental to the study of brain
networks. We developed a method for quantifying axonal orientations at a resolution of~ 15 …