基于FSRCNN补充模块的图像超分辨率重建算法研究

doi:10.3969/j.issn.1000-1158.2023.11.05

Abstract
Figure/Table
References (18)
Related Citation (15)

Download: PDF (2396 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The activation function can increase the nonlinearity of the algorithm and improve the complexity of the algorithm in the super-resolution reconstruction algorithm. Using the ReLU activation function, the algorithm has the advantage of short training time, and in view of the problem that the negative value is zeroed through the ReLU activation function, resulting in the inactivation of some neurons, the inactivated part is re-added to the model through the rReLU function, and the method is named FSRCNN supplementary module. The results show that the peak signal-to-noise ratio of the supplementary module algorithm is 0.1dB higher than that of the original FSRCNN algorithm under the condition of magnification of 4, so the supplementary module can improve the performance of the model and enhance the extraction of information by the model.

Key words： metrology image recovery super-resolution reconstruction FSRCNN model supplementary module rReLU function

Received: 06 September 2022 Published: 17 November 2023

PACS:

TB96

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	CHEN Wei-rui
	HOU Pei-guo

Cite this article:

CHEN Wei-rui,HOU Pei-guo. Research on Image Super-Resolution Reconstruction Algorithm Based on FSRCNN Supplementary Module[J]. Acta Metrologica Sinica, 2023, 44(11): 1667-1672.

URL:

http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2023.11.05 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2023/V44/I11/1667

［9］	He K M, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition ［C］//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 2016.
［11］	Wei Y, Gu S, Li Y, et al. Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training ［C］//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, USA, 2021.
［17］	张建国, 拓洋洋, 蒋瑞娇, 等. Richardson-Lucy算法在模糊图像复原中的改进［J］.计量学报, 2020, 41 (2): 153-158.
［18］	赵朗月, 吴一全. 基于机器视觉的表面缺陷检测方法研究进展［J］. 仪器仪表学报, 2022, 43 (1): 198-219.
［2］	Lew J, Kim E, Heo J P. Pixel-Level Kernel Estimation for Blind Super-Resolution ［J］. IEEE Access, 2021, 9: 152803-152818.
［7］	Dong C, Loy C C, Tang X. Accelerating the Super-Resolution Convolutional Neural Network ［C］//Europe-an Conference on Computer Vision. Amsterdam, Netherlands, 2016 .
［16］	张立国, 蒋轶轩, 田广军. 基于多尺度融合方法的无人机对地车辆目标检测算法研究［J］.计量学报, 2021, 42 (11): 1436-1442.
［1］	Zeng X Y, Lu H C, Zhang C Y. Super Resolution Image Restoration Algorithm: Based on Wavelet and Interpolation［C］//2020 3rd World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM). Shanghai, China, 2020, 95-98.
［3］	Yu J, Gao X, Tao D, et al. A Unified Learning Framework for Single Image Super-Resolution ［J］. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25 (4): 780-792.
［4］	程淑红, 芦嘉鑫, 张典范, 等. 基于环形特征的卷积神经网络轮毂识别［J］.计量学报, 2022, 43 (6): 730-736.
［5］	朱奇光, 董惠茹, 张孟颖. 基于人体动作识别的类人机器人动作模仿［J］.计量学报, 2021, 42 (9): 1136-1141.
［14］	张世辉, 闫晓蕊, 桑榆. 融合残差及通道注意力机制的单幅图像去雨方法［J］.计量学报, 2021, 42 (1): 20-28.
［8］	Shi W, Caballero J, Huszár F, et al. Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network［C］//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA, 2016.
［10］	Lim B, Son S and Kim H, et al. Enhanced Deep Residual Networks for Single Image Super-Resolution ［C］//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Honolulu, USA, 2017.
［12］	Zhu Y, Zhou Z, Liao G, et al. Csrgan: Medical Image Super-Resolution Using A Generative Adversarial Network［C］//2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops). Iowa City, USA, 2020.
	Zhang L G, Jiang T X, Tian G J. Research on Unmanned Aerial Vehicle to Ground Vehicle Target Detection Algorithm Based on Multiscale Fusion Method ［J］. Acta Metrologica Sinica, 2021, 42 (11): 1436-1442.
	Zhang J G, Ta Y Y, Jiang R J, et al. Improvement of the Richardson-Lucy Algorithm in Blurred Image Restoration. Acta Metrologica Sinica, 2020, 41 (2): 153-158.
	Cheng S H, Lu J X, Zhang D F, et al. Wheel Hub Identification of Convolutional Neural Networks Based on Ring Feature［J］. Acta Metrologica Sinica, 2022, 43 (6): 730-736.
	Zhu Q G, Dong H R, Zhang M Y. Humanoid Robot Motion Imitation Based on Human Posture Recognition ［J］. Acta Metrologica Sinica, 2021, 42 (9): 1136-1141.
［6］	Dong C, Loy C C, He K, et al. Image Super-Resolution Using Deep Convolutional Networks ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38 (2): 295-307.
［13］	Zhang Y, Tian Y, Kong Y, et al. Residual Dense Network for Image Restoration ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43 (7): 2480-2495.
	Zhang S H, Yan X R, Sang Y. Single Image Rain Removal Method by Fusing Residual and Channel Attention Mechanism ［J］. Acta Metrologica Sinica, 2021, 42 (1): 20-28.
［15］	Li Y, Si H Q, Zong Y T, et al. Application of Neural Network Based on Real-Time Recursive Learning and Kalman Filter in Flight Data Identification ［J］. International Journal of Aeronautical and Space Sciences, 2021, 22 (6): 1383-1396.
	Zhao L Y, Wu Y Q. Research progress of surface defect detection methods based on machine vision. Chinese Journal of Scientific Instrument, 2022, 43 (1): 198-219.