Search Results for author: Fenglong Song

Found 17 papers, 6 papers with code

DI-Retinex: Digital-Imaging Retinex Theory for Low-Light Image Enhancement

no code implementations4 Apr 2024 Shangquan Sun, Wenqi Ren, Jingyang Peng, Fenglong Song, Xiaochun Cao

Many existing methods for low-light image enhancement (LLIE) based on Retinex theory ignore important factors that affect the validity of this theory in digital imaging, such as noise, quantization error, non-linearity, and dynamic range overflow.

Low-Light Image Enhancement Quantization

Computational Spectral Imaging with Unified Encoding Model: A Comparative Study and Beyond

no code implementations20 Dec 2023 Xinyuan Liu, Lizhi Wang, Lingen Li, Chang Chen, Xue Hu, Fenglong Song, Youliang Yan

Computational spectral imaging is drawing increasing attention owing to the snapshot advantage, and amplitude, phase, and wavelength encoding systems are three types of representative implementations.

Style Projected Clustering for Domain Generalized Semantic Segmentation

no code implementations CVPR 2023 Wei Huang, Chang Chen, Yong Li, Jiacheng Li, Cheng Li, Fenglong Song, Youliang Yan, Zhiwei Xiong

In contrast to existing methods, we instead utilize the difference between images to build a better representation space, where the distinct style features are extracted and stored as the bases of representation.

Clustering Semantic Segmentation

Toward RAW Object Detection: A New Benchmark and a New Model

no code implementations CVPR 2023 Ruikang Xu, Chang Chen, Jingyang Peng, Cheng Li, Yibin Huang, Fenglong Song, Youliang Yan, Zhiwei Xiong

In many computer vision applications (e. g., robotics and autonomous driving), high dynamic range (HDR) data is necessary for object detection algorithms to handle a variety of lighting conditions, such as strong glare.

Autonomous Driving Object +2

Towards Real World HDRTV Reconstruction: A Data Synthesis-based Approach

no code implementations6 Nov 2022 Zhen Cheng, Tao Wang, Yong Li, Fenglong Song, Chang Chen, Zhiwei Xiong

To solve this problem, we propose a learning-based data synthesis approach to learn the properties of real-world SDRTVs by integrating several tone mapping priors into both network structures and loss functions.

Tone Mapping

SJ-HD^2R: Selective Joint High Dynamic Range and Denoising Imaging for Dynamic Scenes

no code implementations20 Jun 2022 Wei Li, Shuai Xiao, Tianhong Dai, Shanxin Yuan, Tao Wang, Cheng Li, Fenglong Song

To further leverage these two paradigms, we propose a selective and joint HDR and denoising (SJ-HD$^2$R) imaging framework, utilizing scenario-specific priors to conduct the path selection with an accuracy of more than 93. 3$\%$.


Real-time Image Enhancer via Learnable Spatial-aware 3D Lookup Tables

no code implementations ICCV 2021 Tao Wang, Yong Li, Jingyang Peng, Yipeng Ma, Xian Wang, Fenglong Song, Youliang Yan

One is a 1D weight vector used for image-level scenario adaptation, the other is a 3D weight map aimed for pixel-wise category fusion.

4k Image Enhancement

Ultra-High-Definition Image Dehazing via Multi-Guided Bilateral Learning

1 code implementation CVPR 2021 Zhuoran Zheng, Wenqi Ren, Xiaochun Cao, Xiaobin Hu, Tao Wang, Fenglong Song, Xiuyi Jia

To address the problem, we propose a novel network capable of real-time dehazing of 4K images on a single GPU, which consists of three deep CNNs.

4k Image Dehazing +2

Multi-Scale Separable Network for Ultra-High-Definition Video Deblurring

1 code implementation ICCV 2021 Senyou Deng, Wenqi Ren, Yanyang Yan, Tao Wang, Fenglong Song, Xiaochun Cao

Although recent research has witnessed a significant progress on the video deblurring task, these methods struggle to reconcile inference efficiency and visual quality simultaneously, especially on ultra-high-definition (UHD) videos (e. g., 4K resolution).

4k Deblurring +1

NODE: Extreme Low Light Raw Image Denoising using a Noise Decomposition Network

no code implementations11 Sep 2019 Hao Guan, Liu Liu, Sean Moran, Fenglong Song, Gregory Slabaugh

In this paper, we propose a multi-task deep neural network called Noise Decomposition (NODE) that explicitly and separately estimates defective pixel noise, in conjunction with Gaussian and Poisson noise, to denoise an extreme low light image.

Image Denoising

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