Recommendation accuracy is a fundamental problem in the quality of the recommendation
system. In this article, we propose an efficient deep matrix factorization (EDMF) with review …

Diffusion model-based inverse problem solvers have demonstrated state-of-the-art
performance in cases where the forward operator is known (ie non-blind). However, the …

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 show that raw degradation features can effectively guide deep restoration models,
providing accurate degradation priors to facilitate better restoration. While networks that do …

The rapid emergence of new portable capturing technologies has significantly increased the
number and diversity of document images acquired for business and personal applications …

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 …

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 …

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 …

Blind image deblurring remains a topic of enduring interest. Learning based approaches,
especially those that employ neural networks have emerged to complement traditional …

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 …