DeepCS: Deep Convolutional Neural Network and SVM Based Single Image Super-Resolution
Computer based patient monitoring systems help in keeping track of the patients’ responsiveness to the treatment over the course of the treatment. Further, development of these kind of healthcare systems that require minimal or no human intervention form one of the most essential elements of smart cities. In order to make it a reality, the computer vision and machine learning techniques provide numerous ways to improve the efficiency of the automated healthcare systems. Image super-resolution (SR) has been an active area of research in the field of computer vision for the past couple of decades. The SR algorithms are offline and independent of image capturing devices making them suitable for various applications such as video surveillance, medical image analysis, remote sensing etc. This paper proposes a learning based SR algorithm for generating high resolution (HR) images from low resolution (LR) images. The proposed approach uses the fusion of deep convolutional neural network (CNN) and support vector machines (SVM) with regression for learning and reconstruction. Learning with deep neural networks exhibit better approximation and support vector machines work well in decision making. The experiments with the retinal images from RIMONE and CHASEDB have shown that the proposed approach outperforms the existing image super-resolution approaches in terms of peak signal to noise ratio (PSNR) as well as mean squared error (MSE).
KeywordsImage super-resolution Deep learning Deep neural networks Rectifier linear units
The authors thankfully acknowledge the financial support provided by The Institution of Engineers (India) for carrying out Research & Development work in this subject.
- 6.He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 346–361. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10578-9_23CrossRefGoogle Scholar
- 10.Yang, M.C., Chu, C.T., Wang, Y.C.F.: Learning sparse image representation with support vector regression for single-image super-resolution. In: IEEE International Conference on Image Processing (ICIP), pp. 1973–1976 (2010)Google Scholar
- 15.Peyrard, C., Mamalet, F., Garcia, C.: A comparison between multi-layer perceptrons and convolutional neural networks for text image super-resolution. In: VISAPP (1), pp. 84–91 (2015)Google Scholar
- 16.Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-2010), pp. 807–814 (2010)Google Scholar
- 17.Nagi, J., Di Caro, G.A., Giusti, A., Nagi, F., Gambardella, L.M.: Convolutional neural support vector machines: hybrid visual pattern classifiers for multi-robot systems. In: the 11th IEEE International Conference on Machine Learning and Applications (ICMLA), vol. 1, pp. 27–32 (2012)Google Scholar
- 18.Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In IEEE Conference on Computer Vision and Pattern Recognition (CVP), pp. 248–255 (2009)Google Scholar
- 19.Chang, H., Yeung, D.Y., Xiong, Y.: Super-resolution through neighbor embedding. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1 (2004)Google Scholar