Design of Cascaded CNN for Medical Image Super Resolution


  • Kajol Singh M Tech Scholar, Bansal Institute of Science and Technology, Bhopal (M.P), India
  • Manish Saxena Professor, Bansal Institute of Science and Technology, Bhopal (M.P), India





The images captured through a camera usually belong to over or under exposed conditions. The reason may be inappropriate lighting conditions or camera resolution. Hence, it is of utmost importance to have a few enhancement techniques that could make these artefacts look better. Hence, the primary objective pertaining to the adjustment and enhancement techniques is to enhance the characteristics of an image. The initial numeric values related to an image get distorted when an image is enhanced. Therefore, enhancement techniques should be designed in such a way that the image quality isn’t compromised. This research work is focused on proposed a network design for deep convolution neural networks for application of super resolution techniques. To improve the complexity of existing techniques this work is intended towards network designs, different filter size and CNN architecture. The CNN model is most effective model for detection and segmentation in image. This model will improve the efficiency of medical image reconstruction from LR to HR. The proposed model showed its efficiency not only PET medical images but also on retinal database and achieved advance results as compared to existing works.


Download data is not yet available.


Tzu-An Song, Samadrita Roy Chowdhury , Fan Yang, and Joyita Dutta. 2020. Super-Resolution PET Imaging Using Convolutional Neural Networks. IEEE Transactions on Computational Imaging, 6, pp. 518-528.

Jun-Ho Choi et al. 2019. Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual Quality. Neuro Computing Elsevier.

Xunxiang Yao, QiangWu, Peng Zhang. 2019. Adaptive rational fractal interpolation function for image super-resolution via local fractal analysis. Image and Vision Computing82, pp. 39-49.

George Seif et al., 2018. Edge-based loss function for single image super-resolution. IEEE. International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 1468-1472.

Xuan Zhu. 2017. Image Super-Resolution Based on Sparse Representation via Direction and Edge Dictionaries. Hindawi Mathematical Problems in engineering.

KlemanGrm and Walter J. Scheirer. 2020. Face Hallucination Using Cascaded Super-Resolution and Identity Priors. IEEE Transactions On Image Processing, 29.

Wang, C., Li, Z., Wu, J., Fan, H., Xiao, G., Zhang, H.: ‘Deep residual haze network for image dehazing and deraining’IEEE Access, 2020, 8, (1), pp. 9488–9500.

Y. Fu, T. Zhang, Y. Zheng, D. Zhang, and H. Huang. 2019. Hyperspectral image super-resolution with optimized rgb guidance, in CVPR.

Zhang Y, Tian Y, Kong Y, Zhong B, Fu Y. 2018. Residual dense network for image super-resolution. IEEE conference on computer vision and pattern recognition (CVPR).

Ahn N., Kang B., Sohn KA. 2018. Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision –ECCV. Lecture Notes in Computer Science, vol 11214. Springer, Cham.

Jan Odstrcilik, Radim Kolar, Attila Budai, Joachim Hornegger, Jiri Jan, Jiri Gazarek, Tomas Kubena, Pavel Cernosek, Ondrej Svoboda, Elli Angelopoulou, „Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database,“ IET Image Processing, Volume 7, Issue 4, June 2013, pp.373-383.




How to Cite

Singh , K. ., & Saxena, M. . (2021). Design of Cascaded CNN for Medical Image Super Resolution. SMART MOVES JOURNAL IJOSCIENCE, 7(3), 22–29.