The computer-based approach is required for the non-invasive detection of chronic liver
diseases that are asymptomatic, progressive, and potentially fatal in nature. In this study, we …

This paper proposes a human-aware deblurring model that disentangles the motion blur
between foreground (FG) humans and background (BG). The proposed model is based on a …

Blind image deblurring aims to recover sharp image from a blurred one while the blur kernel
is unknown. To solve this ill-posed problem, a great amount of image priors have been …

Event-based cameras can measure intensity changes (called'events') with microsecond
accuracy under high-speed motion and challenging lighting conditions. With the active pixel …

In this paper, we present an effective and efficient face deblurring algorithm by exploiting
semantic cues via deep convolutional neural networks (CNNs). As face images are highly …

Blind image deblurring, ie, deblurring without knowledge of the blur kernel, is a highly ill-
posed problem. The problem can be solved in two parts: 1) estimate a blur kernel from the …

We present an effective blind image deblurring method based on a data-driven
discriminative prior. Our work is motivated by the fact that a good image prior should favor …

We present an effective blind image deblurring method based on a data-driven
discriminative prior. Our work is motivated by the fact that a good image prior should favor …

Blind image deblurring is a conundrum because there are infinitely many pairs of latent
image and blur kernel. To get a stable and reasonable deblurred image, proper prior …

Salient edge selection and time-varying regularization are two crucial techniques to
guarantee the success of maximum a posteriori (MAP)-based blind deconvolution. However …