Lensless imaging provides opportunities to design imaging systems free from the constraints
imposed by traditional camera architectures. Due to advances in imaging hardware …

The first mobile camera phone was sold only 20 years ago, when taking pictures with one's
phone was an oddity, and sharing pictures online was unheard of. Today, the smartphone is …

Abstract Machine vision systems that capture images for visual inspection and identification
tasks have to be able to perceive a scene under a range of illumination conditions. To …

Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …

Visual adaptation that can autonomously adjust the response to light stimuli is a basic
function of artificial visual systems for intelligent bionic robots. To improve efficiency and …

Dark environment becomes a challenge for computer vision algorithms owing to insufficient
photons and undesirable noises. Most of the existing studies tackle this by either targeting …

Lacking rich and realistic data, learned single image denoising algorithms generalize poorly
in real raw images that not resemble the data used for training. Although the problem can be …

Burst denoising is now more relevant than ever, as computational photography helps
overcome sensitivity issues inherent in mobile phones and small cameras. A major …

Capturing photographs with wrong exposures remains a major source of errors in camera-
based imaging. Exposure problems are categorized as either:(i) overexposed, where the …

Deep learning-based image denoising approaches have been extensively studied in recent
years, prevailing in many public benchmark datasets. However, the stat-of-the-art networks …