Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Ni, Zhangkai"'
Obtaining pairs of low/normal-light videos, with motions, is more challenging than still images, which raises technical issues and poses the technical route of unpaired learning as a critical role. This paper makes endeavors in the direction of learn
Externí odkaz:
http://arxiv.org/abs/2408.12316
Image deep features extracted by pre-trained networks are known to contain rich and informative representations. In this paper, we present Deep Degradation Response (DDR), a method to quantify changes in image deep features under varying degradation
Externí odkaz:
http://arxiv.org/abs/2406.08377
Deep learning-based methods have significantly influenced the blind image quality assessment (BIQA) field, however, these methods often require training using large amounts of human rating data. In contrast, traditional knowledge-based methods are co
Externí odkaz:
http://arxiv.org/abs/2405.18790
The recent 3D Gaussian splatting (3D-GS) has shown remarkable rendering fidelity and efficiency compared to NeRF-based neural scene representations. While demonstrating the potential for real-time rendering, 3D-GS encounters rendering bottlenecks in
Externí odkaz:
http://arxiv.org/abs/2403.17898
This paper aims to address a common challenge in deep learning-based image transformation methods, such as image enhancement and super-resolution, which heavily rely on precisely aligned paired datasets with pixel-level alignments. However, creating
Externí odkaz:
http://arxiv.org/abs/2402.18192
Neural Radiance Fields (NeRF) have demonstrated impressive potential in synthesizing novel views from dense input, however, their effectiveness is challenged when dealing with sparse input. Existing approaches that incorporate additional depth or sem
Externí odkaz:
http://arxiv.org/abs/2312.09095
High-quality face images are required to guarantee the stability and reliability of automatic face recognition (FR) systems in surveillance and security scenarios. However, a massive amount of face data is usually compressed before being analyzed due
Externí odkaz:
http://arxiv.org/abs/2209.05856
In this paper, a novel and effective image quality assessment (IQA) algorithm based on frequency disparity for high dynamic range (HDR) images is proposed, termed as local-global frequency feature-based model (LGFM). Motivated by the assumption that
Externí odkaz:
http://arxiv.org/abs/2209.02285
Getting rid of the fundamental limitations in fitting to the paired training data, recent unsupervised low-light enhancement methods excel in adjusting illumination and contrast of images. However, for unsupervised low light enhancement, the remainin
Externí odkaz:
http://arxiv.org/abs/2207.00965
Recent years have witnessed the dramatically increased interest in face generation with generative adversarial networks (GANs). A number of successful GAN algorithms have been developed to produce vivid face images towards different application scena
Externí odkaz:
http://arxiv.org/abs/2201.11975