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Journal of ZheJiang University (Engineering Science)  2019, Vol. 53 Issue (6): 1182-1189    DOI: 10.3785/j.issn.1008-973X.2019.06.018
Computer and Aut omation Technology     
Dual-focal camera continuous digital zoom based onCNN and feature extraction
Gui-ran HE(),Qi LI*(),Hua-jun FENG,Zhi-hai XU,Yue-ting CHEN
College of Science and Engineering, Zhejiang University, Hangzhou 310058, China
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A digital continuous zooming scheme mainly for smartphone dual-focal lens was proposed, which was based on extraction and patch-matching of the convolutional neural network feature map, making full use of the shooting information of two different focal length lenses. The high-resolution information of the long-focal image was migrated to the repairable area of the short-focus image, then the zoomed image was produced accordingly. The simulation and experimental results show that the image produced by the proposed method has higher subjective resolution and better visual clarity, compared with the traditional interpolation and super-resolution results. The method can greatly enhance the imaging quality of the dual-focal lens when the magnification factor is between the two lens. For the texture outside the Short-Focal-Len filed but in the Long-Focal-Len field, the proposed method also performs better than the existing methods. Otherwise, the proposed method has good robustness in dealing with the parallax problem of the dual-focal camera.

Key wordsasymmetric dual-focal camera      continuous digital zoom      convolutional neural network (CNN)      feature extraction      super-resolution      image restoration     
Received: 24 October 2018      Published: 22 May 2019
CLC:  TP 751.1  
Corresponding Authors: Qi LI     E-mail:;
Cite this article:

Gui-ran HE,Qi LI,Hua-jun FENG,Zhi-hai XU,Yue-ting CHEN. Dual-focal camera continuous digital zoom based onCNN and feature extraction. Journal of ZheJiang University (Engineering Science), 2019, 53(6): 1182-1189.

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设计一种主要针对智能手机双焦镜头的连续数字变焦方案. 该方案基于卷积神经网络特征层提取和特征块匹配,充分利用2个不同焦距镜头的拍摄信息,将长焦镜头图像的高分辨信息迁移到短焦图像的可修复区域,并以修复后的短焦图像为基础进行数字变焦. 仿真和实验结果表明,相比基于传统插值放大的变焦方案和基于单图像超分辨的变焦方案,所提方案的处理结果拥有更高的主观分辨率和视觉清晰度;当用户给定的变焦倍数在长、短焦镜头倍数之间时,所提方案可以显著提升变焦图像的质量;对于处于长焦相机视场外、短焦相机视场内的纹理,修复效果比现有方法更好;该方法的处理结果对长焦、短焦图像的双目视差大小有着很好的鲁棒性.

关键词: 非对称双焦镜头,  连续数字变焦,  卷积神经网络(CNN),  特征提取,  超分辨成像,  图像恢复 
Fig.1 Texture restoring results of stimulated images with PatchMatch method
Fig.2 Schematic diagram of classical asymmetric dual zooming imaging model
Fig.3 Scenery images used for stimulation experiments
Fig.4 Comparison of stimulation results for proposed texture restoring method and other methods
Fig.5 Comparison of zooming results for different zooming scales using different methods
图像 M =1.3 M =1.5 M =1.7
双线性插值 A+ 本文方法 双线性插值 A+ 本文方法 双线性插值 A+ 本文方法
Ancient 27.45 29.81 27.78 24.32 25.52 24.46 25.62 27.46 27.30
Autumn 25.27 25.93 26.65 21.59 22.03 22.28 23.60 24.27 25.52
Baboon 27.20 28.57 27.54 23.53 24.27 23.62 27.47 28.09 28.16
Building1 27.20 29.33 28.17 23.13 23.93 23.67 24.27 25.57 26.01
Building2 29.26 31.79 29.48 25.66 26.71 25.87 27.45 29.60 29.23
Building3 23.33 25.57 24.89 20.08 21.00 20.95 20.21 21.59 22.19
Butterfly 29.73 34.79 31.23 28.77 31.97 29.97 26.96 31.27 29.54
Fruits 29.32 31.62 30.85 27.12 28.56 28.02 27.38 29.38 30.11
Shanghai 26.07 28.32 27.26 22.21 22.99 22.83 22.88 24.02 24.24
Mountain 29.75 30.80 31.31 26.04 26.43 26.77 27.43 28.17 29.64
Tab.1 Quantitative evaluation of zooming results in stimulation experiments
Fig.6 Texture restoring examination for field out of Long-Focal-Len
Fig.7 Experimental result of real image and verification of parallax problem
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