Event cameras are bio-inspired sensors that capture the per-pixel intensity changes
asynchronously and produce event streams encoding the time, pixel position, and polarity …

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 …

Deep neural networks (DNN) have achieved great success in image restoration. However,
most DNN methods are designed as a black box, lacking transparency and interpretability …

In this paper, we explore the role of Instance Normalization in low-level vision tasks.
Specifically, we present a novel block: Half Instance Normalization Block (HIN Block), to …

Image restoration tasks demand a complex balance between spatial details and high-level
contextualized information while recovering images. In this paper, we propose a novel …

Image deblurring is a classic problem in low-level computer vision with the aim to recover a
sharp image from a blurred input image. Advances in deep learning have led to significant …

Blind image quality assessment (BIQA) for authentically distorted images has always been a
challenging problem, since images captured in the wild include varies contents and diverse …

We present a new end-to-end generative adversarial network (GAN) for single image motion
deblurring, named DeblurGAN-V2, which considerably boosts state-of-the-art deblurring …

We present a general learning-based solution for restoring images suffering from spatially-
varying degradations. Prior approaches are typically degradation-specific and employ the …

Most of the existing learning-based single image super-resolution (SISR) methods are
trained and evaluated on simulated datasets, where the low-resolution (LR) images are …