This paper analyzes, compares, and contrasts technical challenges, methods, and the
performance of text detection and recognition research in color imagery. It summarizes the …

Numerous learning-based approaches to single image deblurring for camera and object
motion blurs have recently been proposed. To generalize such approaches to real-world …

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

In this paper, we tackle the problem of blind image super-resolution (SR) with a reformulated
degradation model and two novel modules. Following the common practices of blind SR, our …

Traditional frame-based cameras inevitably suffer from motion blur due to long exposure
times. As a kind of bio-inspired camera, the event camera records the intensity changes in …

Blind deconvolution is a classical yet challenging low-level vision problem with many real-
world applications. Traditional maximum a posterior (MAP) based methods rely heavily on …

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 …

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 …

Removing pixel-wise heterogeneous motion blur is challenging due to the ill-posed nature
of the problem. The predominant solution is to estimate the blur kernel by adding a prior, but …