Medical imaging methods, such as CT scans, MRI scans, X-rays, and ultrasound imaging,
are widely used for diagnosis in the healthcare domain. However, these methods are often …

The incorporation of physical information in machine learning frameworks is opening and
transforming many application domains. Here the learning process is augmented through …

Calibration-based methods have dominated RAW image denoising under extremely low-
light environments. However, these methods suffer from several main deficiencies: 1) the …

Modeling and synthesizing low-light raw noise is a fundamental problem for computational
photography and image processing applications. Although most recent works have adopted …

Although deep neural networks have achieved astonishing performance in many vision
tasks, existing learning-based methods are far inferior to the physical model-based solutions …

Low-light raw image denoising is an essential task in computational photography, to which
the learning-based method has become the mainstream solution. The standard paradigm of …

Image recognition models that work in challenging environments (eg, extremely dark, blurry,
or high dynamic range conditions) must be useful. However, creating training datasets for …

Capturing images is a key part of automation for high-level tasks such as scene text
recognition. Low-light conditions pose a challenge for high-level perception stacks, which …

Abstract sRGB images are now the predominant choice for pre-training visual models in
computer vision research, owing to their ease of acquisition and efficient storage …

Image denoising is a critical component in a camera's Image Signal Processing (ISP)
pipeline. There are two typical ways to inject a denoiser into the ISP pipeline: applying a …