This paper surveys machine-learning-based super-resolution reconstruction for vortical
flows. Super resolution aims to find the high-resolution flow fields from low-resolution data …

Super resolution (SR) methods typically assume that the low-resolution (LR) image was
downscaled from the unknown high-resolution (HR) image by a fixedideal'downscaling …

Deep learning based methods have dominated super-resolution (SR) field due to their
remarkable performance in terms of effectiveness and efficiency. Most of these methods …

This paper comprehensively reviews the recent development of image deblurring, including
non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques …

We present a simple and effective blind image deblurring method based on the dark
channel prior. Our work is inspired by the interesting observation that the dark channel of …

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 …

In this paper, we address the problem of estimating and removing non-uniform motion blur
from a single blurry image. We propose a deep learning approach to predicting the …

Camera motion introduces motion blur, affecting many computer vision tasks. Dark Channel
Prior (DCP) helps the blind deblurring on scenes including natural, face, text, and low …

We show in this paper that the success of previous maximum a posterior (MAP) based blur
removal methods partly stems from their respective intermediate steps, which implicitly or …

We present an effective blind image deblurring algorithm based on the dark channel prior.
The motivation of this work is an interesting observation that the dark channel of blurred …