At the pinnacle of computational imaging is the co-optimization of camera and algorithm.
This, however, is not the only form of computational imaging. In problems such as imaging …

Since the seminal work of Andrey Kolmogorov in the early 1940's, imaging through
atmospheric turbulence has grown from a pure scientific pursuit to an important subject …

A broad class of problems at the core of computational imaging, sensing, and low-level
computer vision reduces to the inverse problem of extracting latent images that follow a prior …

Many imaging inverse problems $\unicode {x2014} $ such as image-dependent in-painting
and dehazing $\unicode {x2014} $ are challenging because their forward models are …

Imaging at range for the purposes of biometric, scientific, or militaristic applications often
suffer due to degradations by the atmosphere. These degradations, due to the non …

Many image restoration problems are ill-posed in nature, hence, beyond the input image,
most existing methods rely on a carefully engineered image prior, which enforces some …

Incorporating a deep generative model as the prior distribution in inverse problems has
established substantial success in reconstructing images from corrupted observations …

Image inverse problem aims to reconstruct or restore high-quality images from observed
samples or degraded images, with wide applications in imaging sciences. The traditional …

The use of digital imaging devices, ranging from professional digital cinema cameras to
consumer grade smartphone cameras, has become ubiquitous. The acquired image is a …

We present an algorithm to directly solve numerous image restoration problems (eg, image
deblurring, image dehazing, and image deraining). These problems are ill-posed, and the …