Low-light photography presents significant challenges. Multi-image processing methods
have made numerous attempts to obtain high-quality photos yet remain unsatisfactory …

This paper reviews the first challenge on high-dynamic range (HDR) imaging that was part
of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in …

Abstract Machine learning techniques work best when the data used for training resembles
the data used for evaluation. This holds true for learned single-image denoising algorithms …

We present a comprehensive overview of the stereoscopic Intel RealSense RGBD imaging
systems. We discuss these systems' mode-of-operation, functional behavior and include …

Standard digital cameras typically take images with under/overexposed regions because of
their sensors' limited dynamic range. The most common way to capture high dynamic range …

Demosaicking and denoising are the key first stages of the digital imaging pipeline but they
are also a severely ill-posed problem that infers three color values per pixel from a single …

Cell phone cameras have small apertures, which limits the number of photons they can
gather, leading to noisy images in low light. They also have small sensor pixels, which limits …

Real-noise denoising is a challenging task because the statistics of real-noise do not follow
the normal distribution, and they are also spatially and temporally changing. In order to cope …

Synthesizing high dynamic range (HDR) images from multiple low-dynamic range (LDR)
exposures in dynamic scenes is challenging. There are two major problems caused by the …

Abstract High-dynamic-range (HDR) imaging is crucial for many applications. Yet, acquiring
HDR images with a single shot remains a challenging problem. Whereas modern deep …