基于压缩卷积神经网络的图像超分辨率算法

doi:10.16180/j.cnki.issn1007-7820.2020.05.001

Abstract

Abstract:

In order to effectively improve the resolution of depth image, the study proposed a convolutional neural network model based on the Fire Module by referring to the classic SqueezeNet network structure. The proposed algorithm implemented mapping and transformation directly from low-resolution images to high-resolution images. As a nonlinear mapping module of the network, Fire Module learned the deep features of the image while reducing the parameters. To avoid interpolation preprocessing, a deconvolution layer was introduced in the output layer of the network to achieve a final 3 times up-sampling and high resolution image output. Experiments showed that the super-resolution image obtained by the deconvolution algorithm of the convolutional neural network model based on Fire Module was richer in detail, and the evaluation of objective index PSNR value and SSIM value was also superior to other algorithms.

Key words: image processing, super-resolution reconstruction, convolution neural network, deconvolution, residual block, inception block

CLC Number:

TP391

QIN Xing,GAO Xiaoqi,CHEN Bin. Image Super-resolution Algorithm Based on SqueezeNet Convolution Neural Network[J].Electronic Science and Technology, 2020, 33(5): 1-8.

Figures/Tables 13

Figure 1.

Figure 2.

Figure 3.

Table 1

Figure 4.

Figure 5.

Table 2

Figure 6.

Figure 7.

Table 3

Table 4

Figure 8.

Figure 9.

References 18

[1]	曾凯, 丁世飞 . 图像超分辨率重建的研究进展[J]. 计算机工程与应用, 2017,53(16):29-35.
	Zeng Kai, Ding Shifei . Advances in image super-resolution reconstruction[J]. Computer Engineering and Applica-tions, 2017,53(16):29-35.
[2]	沈丽, 韩彦芳 . 基于稀疏表示的超分辨率图像重建[J]. 电子科技, 2015,28(9):144-148
	Shen Li, Han Yanfang . Image super-resolution reconstruction based on sparse representatio[J]. Electronic Science and Technology, 2015,28(9):144-148.
[3]	Irani M, Peleg S . Super resolution from image sequences[J]. ICPR-C, 1990,90(11):115-120.
[4]	李影, 徐伯庆 . 一种基于压缩感知的迭代重建算法[J]. 电子科技, 2016,29(11):129-134.
	Li Ying, Xu Boqing . An iterative image reconstruction algorithm based on compressed sensing[J]. Electronic Science and Technology, 2016,29(11):129-134.
[5]	Li Y M, Pedrycz W . Fuzzy finite qutomata and fuzzy regular expressions with membership values in lattice ordered monoids[J]. Fuzzy Sets and Systems, 2005,156(10):68-92.
[6]	张磊, 杨建峰, 薛彬 , 等. 改进的最大后验概率估计法实现单幅图像超分辨率重建[J]. 激光与光电子学进展, 2011,48(1):78-85.
	Zhang Lei, Yang Jianfeng, Xue Bin , et al. Modified MAP algorithm for single frame super-resolution reconstruction[J]. Laser & Optoelectronics Progress, 2011,48(1):78-85.
[7]	Chang H, Yeung D Y, Xiong Y. Super-resolution through neighbor embedding [C]. Washington DC:Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004.
[8]	Gao X, Zhang K, Tao D , et al. Image super-resolution with sparse neighbor embedding[J]. IEEE Transactions on Image Processing, 2012,21(7):78-85.
[9]	Timofte R, De V, Gool L V. Anchored neighborhood regression for fast example-based super-resolution [C]. Sydney:IEEE International Conference on Computer Vision, 2013.
[10]	Yang J, Wright J, Huang T S et al.Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing, 2010,19(7):2861-2873.
[11]	Dong C, Loy C C, He K , et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Anal Mach Intell, 2014,38(2):295-307.
[12]	Dong C, Deng Y, Chen C L, et al. Compression artifacts reduction by a deep convolutional network [C]. Santiago:IEEE International Conference on Computer Vision, 2015.
[13]	王一宁, 秦品乐, 李传朋 , 等. 基于残差神经网络的图像超分辨率改进算法[J]. 计算机应用, 2018,22(1):246-254.
	Wang Yining, Qin Pinle, Li Chuanpeng . Improved algorithm of image super resolution based on residual neural network[J]. Journal of Computer Applications, 2018,22(1):246-254.
[14]	Kim J, Lee J K, Lee K M. Accurate image super-resolution using very deep convolutional networks [C]. Las Vegas:Computer Vision and Pattern Recognition, 2016.
[15]	Iandola F N, Han S. SqueezeNet:AlexNet-level accuracy with 50× fewer parameters and <0.5MB model size [C]. San Juan:International Conference on Learning Representations, 2016.
[16]	He Kaiming, Sun Jian. Convolutional neural networks at constrained time cost [C]. Boston:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.
[17]	Timofte R, De V, Gool L V. Anchored neighborhood regression for fast example-based super-resolution [C]. Sydney:Processing of the IEEE International Conference on Computer Vision, 2013.
[18]	周壮, 鲍伟龙 . 超分辨率成像系统的FPGA控制研究[J]. 电子科技, 2011,24(10):122-126.
	Zhou Zhuang, Bao Weilong . Research on FPGA control in super-resolution color imaging system[J]. Electronic Science and Technology, 2011,24(10):122-126.

Name	Number	Size	Pad
Conv₁	56	5×5	0
Conv₂	12	1×1	0
Conv_{2_1}	12	1×1	0
Conv_{2_2}	12	3×3	1
Conv₃	56	1×1	0
Conv₄	1	9×9	4

	PSNR			SSIM
	Art	Books	Dolls	Art	Books	Dolls
bicubic	31.14	31.21	32.59	0.928 0	0.958 5	0.952 3
Line3-1	33.15	32.06	33.41	0.956 9	0.965 9	0.958 6
Line3	33.20	32.30	33.53	0.957 2	0.966 6	0.959 1
FireNet1-3-5	33.28	32.32	33.61	0.959 1	0.968 0	0.960 3
FireNet1-3	33.22	32.20	33.48	0.956 9	0.966 2	0.958 3

Image	Index	Bicubic	ScSR	ANR	SRCNN	Proposed
Butterfly	SSIM	0.821 4	0.857 4	0.873 6	0.902 9	0.920 0
Butterfly	PSNR	24.03	25.46	25.98	27.57	28.49
Woman	SSIM	0.889 2	0.909 4	0.917 1	0.923 9	0.930 0
Woman	PSNR	28.56	29.86	30.45	30.91	31.61
Comic	SSIM	0.688 9	0.756 9	0.760 3	0.776 1	0.786 9
Comic	PSNR	23.11	23.89	24.00	24.31	24.54
Face	SSIM	0.797 7	0.819 8	0.823 1	0.820 1	0.827 6
Face	PSNR	32.80	33.38	33.57	33.52	33.72
zebra	SSIM	0.795 0	0.841 2	0.845 6	0.794 6	0.852 2
zebra	PSNR	26.17	26.62	28.54	28.82	29.09

Image	Index	Bicubic	ScSR	ANR	SRCNN	Proposed
Art	SSIM	0.871 9	0.891 5	0.902 1	0.907 5	0.915 5
Art	PSNR	30.61	31.66	32.06	33.64	33.14
Books	SSIM	0.807 7	0.818 5	0.835 3	0.841 5	0.847 2
Books	PSNR	28.46	29.21	29.51	29.96	30.17
Dolls	SSIM	0.853 3	0.872 5	0.885 8	0.889 3	0.896 8
Dolls	PSNR	30.04	31.08	31.47	31.85	32.19
Laundry	SSIM	0.839 1	0857 7	0.870 8	0.877 2	0.885 9
Laundry	PSNR	28.62	29.80	30.16	30.89	31.49
Reindeer	SSIM	0.879 0	0.884 7	0.899 4	0.905 4	0.910 9
Reindeer	PSNR	31.51	32.66	32.85	33.62	33.95

Image Super-resolution Algorithm Based on SqueezeNet Convolution Neural Network

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 13

References 18

Related Articles 15

Metrics

Comments

Recommended 10

[1]	ZONG Shengkang,CHENG Jianpeng,ZHANG Xiliang. Automatic Detection Method of Crane Track Altitude Difference Based on Spot Position [J]. Electronic Science and Technology, 2022, 35(1): 21-28.
[2]	SONG Zhangming,HE Huiyong,HUANG Yuejun. Study on On-Line Detection of Surface Defects of Flat Enameled Wire [J]. Electronic Science and Technology, 2021, 34(5): 72-78.
[3]	ZHU Bin,LIU Zilong. Image Classification Method Using Convolutional Neural Network Based on New Initial Module [J]. Electronic Science and Technology, 2021, 34(2): 52-56.
[4]	YAN Chao,SUN Zhanquan,TIAN Engang,ZHAO Yangyang,FAN Xiaoyan. Research Progress of Medical Image Segmentation Based on Deep Learning [J]. Electronic Science and Technology, 2021, 34(2): 7-11.
[5]	CHEN Wenyou,ZHANG Wei,SHI Xiaofan,SONG Fang. A Semi-Dense 3D Reconstruction ORB-SLAM Algorithm with Improved ORB Feature Matching [J]. Electronic Science and Technology, 2021, 34(12): 62-67.
[6]	SHI Weizhong,CAO Weiwei,FAN Yanming,DONG Jiajun,CHEN Shu,XIAO Hao. FPGA-Based Real-Time Edge Detection and its Implementation for Deep-Space Images [J]. Electronic Science and Technology, 2020, 33(5): 45-49.
[7]	ZHANG Tianhao,LI Haolin. A Method of Laser Center Extraction Based on Gaussian Fitting [J]. Electronic Science and Technology, 2020, 33(5): 72-76.
[8]	XIE Zhixuan,YAO Hongbing,FAN Ning,CHEN Feng. Connected Domain Label Detection Algorithm for Multi-target Lens [J]. Electronic Science and Technology, 2020, 33(4): 50-54.
[9]	ZHAN Yan,ZHANG Juan. A Domain Adaptive Depth Estimation Method for Structural Perception Loss [J]. Electronic Science and Technology, 2020, 33(12): 12-16.
[10]	WANG Wenkai. Blur Kernel Estimation Algorithm Based on Cepstrum and Spectrum Analysis [J]. Electronic Science and Technology, 2019, 32(2): 4-8.
[11]	HE Wenjie,LIU Jingbiao,PAN Mian,LÜ Shuaishuai. Natural Scene Text Recognition Algorithm Based on Attention-CTC [J]. Electronic Science and Technology, 2019, 32(12): 32-36.
[12]	YANG Yunhui. Research on Workpiece Location Technology Based on Monocular Vision [J]. Electronic Science and Technology, 2019, 32(12): 72-75.
[13]	XIONG Hai-Peng, CHEN Xiang-Xiang, CHEN Chun-Wei. Text Location in Image Based on Convolution Neural Network [J]. , 2018, 31(1): 50-.
[14]	KANG Xiu-Juan, SHI Zhan. A System of Foreign Object Localization Based on Graphics Processing [J]. , 2018, 31(1): 71-.
[15]	ZHANG Jiajia，YANG Zhenchao，ZHOU Lü. The Cores Sorting System Research Using Image Processing Based on Matlab [J]. , 2017, 30(9): 50-.