Shengrong Zhao

Orcid: 0000-0003-0965-0918

According to our database1, Shengrong Zhao authored at least 27 papers between 2014 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
LMSFF: Lightweight multi-scale feature fusion network for image recognition under resource-constrained environments.
Expert Syst. Appl., 2025

2024
Lightweight super-resolution via multi-group window self-attention and residual blueprint separable convolution.
Multim. Syst., October, 2024

DAR-MVSNet: a novel dual attention residual network for multi-view stereo.
Signal Image Video Process., September, 2024

A lightweight visual mamba network for image recognition under resource-limited environments.
Appl. Soft Comput., 2024

Learning Fine-Grained Information Alignment for Calibrated Cross-Modal Retrieval.
Proceedings of the IEEE International Conference on Acoustics, 2024

2023
Cascade Cost Volume Multi-View Stereo Network with Transformer and Pseudo 3D.
Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2023

LMBNet: Lightweight Multiple Branch Network for Recognition of HER2 Expression Levels.
Proceedings of the International Neural Network Society Workshop on Deep Learning Innovations and Applications, 2023

LCCN: A Lightweight Capture Context Network for Image Super-Resolution.
Proceedings of the International Joint Conference on Neural Networks, 2023

Image super resolution via multi-regularization combining hybrid Tikhonov-TV prior and deep denoiser prior.
Proceedings of the 35th IEEE International Conference on Tools with Artificial Intelligence, 2023

GA-Net: Gated Attention Mechanism Based Global Refinement Network for Image Inpainting.
Proceedings of the 29th IEEE International Conference on Parallel and Distributed Systems, 2023

GAF-GAN: Gated Attention Feature Fusion Image Inpainting Network Based on Generative Adversarial Network.
Proceedings of the 29th IEEE International Conference on Parallel and Distributed Systems, 2023

LDVNet: Lightweight and Detail-Aware Vision Network for Image Recognition Tasks in Resource-Constrained Environments.
Proceedings of the 29th IEEE International Conference on Parallel and Distributed Systems, 2023

2022
Multi-fidelity and learning-regularization for single image super resolution.
J. Frankl. Inst., 2022

MLSR: Missing information-based fidelity and learned regularization for single-image super-resolution.
Comput. Electr. Eng., 2022

WPNet: Wide Pyramid Network for Recognition of HER2 Expression Levels in Breast Cancer Evaluation.
Proceedings of the International Joint Conference on Neural Networks, 2022

2021
Salt and Pepper Noise Removal Method Based on a Detail-Aware Filter.
Symmetry, 2021

2020
Learning regularization and intensity-gradient-based fidelity for single image super resolution.
CoRR, 2020

2019
Salt and Pepper Noise Suppression for Medical Image by Using Non-local Homogenous Information.
Proceedings of the Cognitive Internet of Things: Frameworks, Tools and Applications, 2019

Removal of Salt-and-Pepper Noise from Bimodal Images Using a Non-Symmetry and Anti-Packing Model.
J. Medical Imaging Health Informatics, 2019

2017
The NAMlet transform: A novel image sparse representation method based on non-symmetry and anti-packing model.
Signal Process., 2017

2016
A Generalized Detail-Preserving Super-Resolution method.
Signal Process., 2016

Multiframe super-resolution based on half-quadratic prior with artifacts suppress.
J. Vis. Commun. Image Represent., 2016

Super-resolving barcode images with an edge-preserving variational Bayesian framework.
J. Electronic Imaging, 2016

Integrating the Missing Information Estimation into Multi-frame Super-Resolution.
Circuits Syst. Signal Process., 2016

2015
A novel multi-image super-resolution reconstruction method using anisotropic fractional order adaptive norm.
Vis. Comput., 2015

2014
A novel reconstruction model for multi-frame super-resolution image based on lmix prior.
Comput. Electr. Eng., 2014

Multi image super resolution reconstruction using a novel degradation model.
Proceedings of the International Conference on Audio, 2014


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