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Computer Science

D-Index
33
Citations
4632
World Ranking
12688
National Ranking
1557

Overview

Feng Shao is a researcher affiliated with Ningbo University in China, specializing in computer science and engineering. Their work primarily focuses on areas related to computer vision and pattern recognition, with extensive contributions in media technology as well as environmental and cognitive-related fields.

The main academic fields of Feng Shao's research include:

  • Computer Science
  • Engineering

Within these broader fields, subfields of particular interest in their publications are:

  • Computer Vision and Pattern Recognition
  • Media Technology
  • Environmental Engineering
  • Cognitive Neuroscience
  • Ecology

The thematic topics they have addressed frequently in their work consist of:

  • Advanced Image Fusion Techniques
  • Visual Attention and Saliency Detection
  • Image and Video Quality Assessment
  • Image Enhancement Techniques
  • Advanced Image Processing Techniques
  • Remote-Sensing Image Classification
  • Image and Signal Denoising Methods

Feng Shao has published numerous papers in well-known venues, contributing significantly to specific journals associated with imaging and remote sensing technologies. Most frequent publication venues for their work include:

  • IEEE Transactions on Geoscience and Remote Sensing
  • IEEE Transactions on Multimedia
  • IEEE Transactions on Circuits and Systems for Video Technology
  • Journal of Visual Communication and Image Representation
  • IEEE Transactions on Instrumentation and Measurement

Among their recent scholarly papers are the following:

  • Underwater Image Enhancement Quality Evaluation: Benchmark Dataset and Objective Metric, 2022, IEEE Transactions on Circuits and Systems for Video Technology
  • A Large-Scale Benchmark Data Set for Evaluating Pansharpening Performance: Overview and Implementation, 2020, IEEE Geoscience and Remote Sensing Magazine
  • Unsupervised Decomposition and Correction Network for Low-Light Image Enhancement, 2022, IEEE Transactions on Intelligent Transportation Systems
  • Single Image Super-Resolution Quality Assessment: A Real-World Dataset, Subjective Studies, and an Objective Metric, 2022, IEEE Transactions on Image Processing
  • Vision Transformer for Pansharpening, 2022, IEEE Transactions on Geoscience and Remote Sensing

Collaboration has been a consistent element in Feng Shao's research activities. Frequent co-authors include:

  • Qiuping Jiang
  • Hangwei Chen
  • Xiangchao Meng
  • Xiongli Chai
  • Yo-Sung Ho

Best Publications

  • Underwater Image Enhancement Quality Evaluation: Benchmark Dataset and Objective Metric

    Unknown

  • Perceptual Full-Reference Quality Assessment of Stereoscopic Images by Considering Binocular Visual Characteristics

    Feng Shao;Weisi Lin;Shanbo Gu;Gangyi Jiang

  • Optimizing Multistage Discriminative Dictionaries for Blind Image Quality Assessment

    Qiuping Jiang;Feng Shao;Weisi Lin;Ke Gu

  • Unified No-Reference Quality Assessment of Singly and Multiply Distorted Stereoscopic Images

    Qiuping Jiang;Feng Shao;Wei Gao;Zhuo Chen

  • A Large-Scale Benchmark Data Set for Evaluating Pansharpening Performance: Overview and Implementation

    Xiangchao Meng;Yiming Xiong;Feng Shao;Huanfeng Shen

  • Single Image Super-Resolution Quality Assessment: A Real-World Dataset, Subjective Studies, and an Objective Metric

    Unknown

  • Unsupervised Decomposition and Correction Network for Low-Light Image Enhancement

    Unknown

  • Asymmetric Coding of Multi-View Video Plus Depth Based 3-D Video for View Rendering

    Feng Shao;Gangyi Jiang;Mei Yu;Ken Chen

  • Full-Reference Quality Assessment of Stereoscopic Images by Learning Binocular Receptive Field Properties

    Feng Shao;Kemeng Li;Weisi Lin;Gangyi Jiang

  • CGMDRNet: Cross-Guided Modality Difference Reduction Network for RGB-T Salient Object Detection

    Unknown

  • Toward a Blind Deep Quality Evaluator for Stereoscopic Images Based on Monocular and Binocular Interactions

    Feng Shao;Weijun Tian;Weisi Lin;Gangyi Jiang

  • Two-Branch Deep Neural Network for Underwater Image Enhancement in HSV Color Space

    Junkang Hu;Qiuping Jiang;Runmin Cong;Wei Gao

  • Joint Bit Allocation and Rate Control for Coding Multi-View Video Plus Depth Based 3D Video

    Feng Shao;Gangyi Jiang;Weisi Lin;Mei Yu

  • Subjective quality analyses of stereoscopic images in 3DTV system

    Junming Zhou;Gangyi Jiang;Xiangying Mao;Mei Yu

  • New fragile watermarking method for stereo image authentication with localization and recovery

    Mei Yu;Jing Wang;Gangyi Jiang;Zongju Peng

  • BLIQUE-TMI: Blind Quality Evaluator for Tone-Mapped Images Based on Local and Global Feature Analyses

    Qiuping Jiang;Feng Shao;Weisi Lin;Gangyi Jiang

  • A SAR-to-Optical Image Translation Method Based on Conditional Generation Adversarial Network (cGAN)

    Yu Li;Randi Fu;Xiangchao Meng;Wei Jin

  • Blind Image Quality Assessment for Stereoscopic Images Using Binocular Guided Quality Lookup and Visual Codebook

    Feng Shao;Weisi Lin;Shanshan Wang;Gangyi Jiang

  • Three-dimensional visual comfort assessment via preference learning

    Qiuping Jiang;Feng Shao;Gangyi Jiang;Mei Yu

  • Blind Image Quality Measurement by Exploiting High-Order Statistics With Deep Dictionary Encoding Network

    Qiuping Jiang;Wei Gao;Shiqi Wang;Guanghui Yue

  • A depth perception and visual comfort guided computational model for stereoscopic 3D visual saliency

    Qiuping Jiang;Feng Shao;Gangyi Jiang;Mei Yu

  • Difference of Gaussian statistical features based blind image quality assessment: A deep learning approach

    Yaqi Lv;Gangyi Jiang;Mei Yu;Haiyong Xu

  • No-reference Stereoscopic Image Quality Assessment Using Binocular Self-similarity and Deep Neural Network

    Yaqi Lv;Mei Yu;Gangyi Jiang;Feng Shao

  • Learning Blind Quality Evaluator for Stereoscopic Images Using Joint Sparse Representation

    Feng Shao;Kemeng Li;Weisi Lin;Gangyi Jiang

  • Learning Receptive Fields and Quality Lookups for Blind Quality Assessment of Stereoscopic Images

    Feng Shao;Weisi Lin;Shanshan Wang;Gangyi Jiang

Frequent Co-Authors

Yo-Sung Ho
Yo-Sung Ho Gwangju Institute of Science and Technology
Weisi Lin
Weisi Lin Nanyang Technological University
Qionghai Dai
Qionghai Dai Tsinghua University
Ke Gu
Ke Gu Beijing University of Technology
Shiqi Wang
Shiqi Wang City University of Hong Kong
Shutao Li
Shutao Li Hunan University
Huanfeng Shen
Huanfeng Shen Wuhan University
Sam Kwong
Sam Kwong Lingnan University
Runmin Cong
Runmin Cong Shandong University
Xinghao Ding
Xinghao Ding Xiamen University

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