研究方向：计算机视觉、机器视觉、机器学习、人工智能、大数据分析。

联系方式：dengjiehang@gdut.edu.cn

 

工作经历:

2005年4月-2006年8月在深圳迈瑞医疗有限公司从事软件开发工程师工作。

2009年10月-2010年3月在日本国立福井大学从事博士辅助研究员工作。

2010年6月至今在广东工业大学计算机学院工作，一直从事图像、视频处理算法研究工作，特别是计算机视觉、机器视觉、机器学习、人工智能、大数据分析的相关工作。

 

主要论文：

[1] Deng J, Wei H, Lai Z, et al. Spatial transform depthwise over-parameterized convolution recurrent neural network for license plate recognition in complex environment[J]. Journal of Computing and Information Science in Engineering, 2023, 23(1): 011010.

[2] Deng J, Chen H, Yuan Z, et al. An enhanced image quality assessment by synergizing superpixels and visual saliency[J]. Journal of Visual Communication and Image Representation, 2022, 88: 103610.

[3] Gu G ,  Lu H ,  Deng J , et al. A synergetic image encryption method based on discrete fractional random transform and chaotic maps[J]. Multimedia Tools and Applications:2022，1-25. 

[4] Gu G, Gan S, Deng J*(通讯作者), et al. Automated diatom detection in forensic drowning diagnosis using a single shot multibox detector with plump receptive field[J]. Applied soft computing, 2022, 122: 108885. 

[5] Deng J, Guo W, Zhao Y, et al. Identification of diatom taxonomy by a combination of region-based full convolutional network, online hard example mining, and shape priors of diatoms[J]. International Journal of Legal Medicine, 2021, 135(6): 2519-2530.

[6] Deng J, Wei H, He D, et al. A coarse to fine framework for recognizing and locating multiple diatoms with highly complex backgrounds in forensic investigation[J]. Multimedia Tools and Applications, 2021: 1-19.

[7] Deng J, Liu J, Wu C, et al. A novel framework for classifying leather surface defects based on a parameter optimized residual network[J]. IEEE Access, 2020, 8: 192109-192118.

[8] Deng J, He D, Zhuo J, et al. Deep learning network-based recognition and localization of diatom images against complex background[J]. Nan Fang yi ke da xue xue bao= Journal of Southern Medical University, 2020, 40(2): 183-189.

[9] Dahuang F, Lei D, Jiehang D. Automatic Detection and Localization of Surface Defects for Whole Piece of Ultrahigh-definition Leather Images[C]//2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS). IEEE, 2019: 229-232.

[10] Deng J, Yang J, Weng S, et al. Copy-move forgery detection robust to various transformation and degradation attacks[J]. KSII Transactions on Internet and Information Systems (TIIS), 2018, 12(9): 4467-4486.

[11] Deng J, Jiang Z, Wang C, et al. Objective evaluations of common and low dose CT images under considerations of local correlations[C]//2015 International Conference on Computers, Communications, and Systems (ICCCS). IEEE, 2015: 201-205.

[12] Deng J , Min Q , Qiao G , et al. Analysis of a 3D Nonlinear Diffusion-based Filter with an Optimization Strategy Based on Low-dose MDCT Images[J]. Journal of Information and Computational Science, 2013, 10(11):3401-3409.

[13] Deng J ,  Qian M ,  Qiao G , et al. Analysis of 3D Linear and Non-linear Filtering Effects Based on 3D MDCT Abdominal Images[J]. Journal of information and computational science, 2013, 10(9):2719-2726.

[14] Deng J, Li Z, Lv Z, et al. Analysis of Filtering Effects Based on Low Dose 3D Hepatic MDCT Images by Applying an Optimized Feature Preserving Strategy[C]//2012 Fourth International Conference on Digital Home. IEEE, 2012: 99-103.

[15] Deng J, Qian M, Qiao G, et al. Reducing X-Ray Exposure of 3D Hepatic MDCT Images by Applying an Optimized Feature Preserving Strategy[C]//Proceedings of the 2011 2nd International Congress on Computer Applications and Computational Science. Springer, Berlin, Heidelberg, 2012: 287-293.

[16] Deng J, Qian M, Qiao G, et al. Analysis of Image Quality Assessment with Markov Random Field Oriented on Low Dose CT Images[J]. Sensors & Transducers, 2014, 169(4): 193.

[17] Susuki Y, Deng J, Hiratsuka K, et al. An adaptive 3D filter to improve the noise level of Low-Dose 3D MDCT[J]. 電子情報通信学会技術研究報告. MI, 医用画像, 2009, 108(385): 641-644.

[18] Deng J, Susuki Y, Hiratsuka K, et al. Analysis of 3D linear and non-linear filtering effects based on 3D MDCT medical images[C]//2008 International Conference on Computer Science and Software Engineering. IEEE, 2008, 6: 149-152.

[19] Deng J, Hiratsuka K, Ishida T, et al. Improvement of low-dose MDCT images by applying a novel adaptive median filter with local averaging[J]. International Journal of Intelligent Computing in Medical Sciences & Image Processing, 2009, 3(1): 31-42.

[20] Deng J, Hiratsuka K, Ishida T, et al. Improvement of low-dose MDCT images by applying a novel adaptive median filter with local averaging[J]. International Journal of Intelligent Computing in Medical Sciences & Image Processing, 2009, 3(1): 31-42.

[21] Weng S, Pan J S, Jiehang D, et al. Pairwise IPVO-based reversible data hiding[J]. Multimedia Tools and Applications, 2018, 77(11): 13419-13444.

[22] Weng S, Pan J S, Deng J. Invariability of Remainder Based Reversible Watermarking[J]. J. Netw. Intell., 2016, 1(1): 16-22.

 

科研项目：

(1) 横向项目，607210434，复杂地形物识别和分析研究，2021.1-2022.12,10万，结题

(2) 横向项目，607170441，皮革智能识别，2018.1-2018.12，15万，结题。

(3) 广东省自然科学基金，GK4200022，结构感知引导的复杂背景干扰硅藻定位与识别，2021.1-2021.12,2万元，结题。

(4) 国家自然科学基金青年项目，61202267、多种先验知识融合的腹部低剂量CT 3D 图像恢复算法研究、2012/01-2015/12、23万元、结题。

(5) 广东省自然科学基金博士启动项目，S2011040004295、压缩感知的低剂量 MDCT 快速精确重建研究，2011/10 – 2013/10、3万元、结题。