158 citations · 177 across the 14 of their papers we have counts for
14 papers
Wavelet-Domain Masked Image Modeling for Color-Consistent HDR Video Reconstruction
Yang Zhang, Zhangkai Ni, Wenhan Yang +1
High Dynamic Range (HDR) video reconstruction aims to recover fine brightness, color, and details from Low Dynamic Range (LDR) videos. However, existing methods often suffer from c…
Structural Similarity-Inspired Unfolding for Lightweight Image Super-Resolution
Zhangkai Ni, Yang Zhang, Wenhan Yang +3
Major efforts in data-driven image super-resolution (SR) primarily focus on expanding the receptive field of the model to better capture contextual information. However, these meth…
Unrolled Decomposed Unpaired Learning for Controllable Low-Light Video Enhancement
Lingyu Zhu, Wenhan Yang, Baoliang Chen +4
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…
DDR: Exploiting Deep Degradation Response as Flexible Image Descriptor
Juncheng Wu, Zhangkai Ni, Hanli Wang +3
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…
Opinion-Unaware Blind Image Quality Assessment using Multi-Scale Deep Feature Statistics
Zhangkai Ni, Yue Liu, Keyan Ding +3
Deep learning-based methods have significantly influenced the blind image quality assessment (BIQA) field, however, these methods often require training using large amounts of huma…
Misalignment-Robust Frequency Distribution Loss for Image Transformation
Zhangkai Ni, Juncheng Wu, Zian Wang +3
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 a…
ColNeRF: Collaboration for Generalizable Sparse Input Neural Radiance Field
Zhangkai Ni, Peiqi Yang, Wenhan Yang +3
Neural Radiance Fields (NeRF) have demonstrated impressive potential in synthesizing novel views from dense input, however, their effectiveness is challenged when dealing with spar…
High Dynamic Range Image Quality Assessment Based on Frequency Disparity
Yue Liu, Zhangkai Ni, Shiqi Wang +2
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 f…
Just Noticeable Difference Modeling for Face Recognition System
Yu Tian, Zhangkai Ni, Baoliang Chen +4
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 massi…
Cycle-Interactive Generative Adversarial Network for Robust Unsupervised Low-Light Enhancement
Zhangkai Ni, Wenhan Yang, Hanli Wang +3
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…
Generalized Visual Quality Assessment of GAN-Generated Face Images
Yu Tian, Zhangkai Ni, Baoliang Chen +3
Recent years have witnessed the dramatically increased interest in face generation with generative adversarial networks (GANs). A number of successful GAN algorithms have been deve…
CSformer: Bridging Convolution and Transformer for Compressive Sensing
Dongjie Ye, Zhangkai Ni, Hanli Wang +3
Convolution neural networks (CNNs) have succeeded in compressive image sensing. However, due to the inductive bias of locality and weight sharing, the convolution operations demons…
Unpaired Image Enhancement with Quality-Attention Generative Adversarial Network
Zhangkai Ni, Wenhan Yang, Shiqi Wang +2
In this work, we aim to learn an unpaired image enhancement model, which can enrich low-quality images with the characteristics of high-quality images provided by users. We propose…
Towards Unsupervised Deep Image Enhancement with Generative Adversarial Network
Zhangkai Ni, Wenhan Yang, Shiqi Wang +2
Improving the aesthetic quality of images is challenging and eager for the public. To address this problem, most existing algorithms are based on supervised learning methods to lea…