Memristive continuous Hopfield neural network circuit for image restoration
- 66 Downloads
Image restoration (IR) methods based on neural network algorithms have shown great success. However, the hardware circuits that can perform real-time IR task with high-effective analog computation are few in the literature. To address such problem, we propose a memristor-based continuous Hopfield neural network (HNN) circuit for processing the IR task in this work. In our circuit, a single memristor crossbar array is used to represent synaptic weights and perform matrix operations. Current feedback operation amplifiers are utilized to achieve integral operation and output function. Given these designs, the proposed circuit can perform continuous recursive operations in parallel and process different optimization problems with the programmability of the memristor array. On the basis of the proposed circuit, binary and greyscale image restorations are conducted through self-organizing network operations, providing a hardware implementation platform for IR tasks. Comparative simulations show the designed HNN circuit provides effective improvements in terms of speed and accuracy compared with software simulation. Moreover, the hardware circuit shows good robustness to memristive variation and input noise.
KeywordsMemristor Circuit design Hopfield neural network Image restoration
This work was supported by the National Natural Science Foundation of China under Grant 61876209 and the National Key R&D Program of China under Grant 2017YFC1501301.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- 1.Akbarizadeh G, Tirandaz Z, Kooshesh M (2014) A new curvelet-based texture classification approach for land cover recognition of sar satellite images. Malays J Comput Sci 27(3):218–239Google Scholar
- 2.Akbarizadeh G, Rangzan K, Kabolizadeh M et al (2016) Effective supervised multiple-feature learning for fused radar and optical data classification. IET Radar Sonar Navig 11(5):768–777Google Scholar
- 4.Aswathi V, Mathew J (2015) A review on image restoration in medical images. Compusoft 4(4):1588Google Scholar
- 5.Bae W, Yoo J, Chul Ye J (2017) Beyond deep residual learning for image restoration: persistent homology-guided manifold simplification. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 145–153Google Scholar
- 22.Li Y, Zhong Y, Zhang J, Xu L, Wang Q, Sun H, Tong H, Cheng X, Miao X (2014) Activity-dependent synaptic plasticity of a chalcogenide electronic synapse for neuromorphic systems. Sci Rep 4(6184):4906Google Scholar
- 25.Lim B, Son S, Kim H, Nah S, Mu Lee K (2017) Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 136–144Google Scholar
- 26.Ling C, Li C, Huang T, Wen S, Chen Y (2015) Memristor crossbar array for image storing. In: International symposium on advances in neural networks-isnn, vol 2015, pp 166–173Google Scholar
- 28.Ma W, Caí F, Du C, Jeong Y, Zidan M, Lu WD (2016) Device nonideality effects on image reconstruction using memristor arrays. In: 2016 IEEE international electron devices meeting (IEDM), IEEE, pp 16–7Google Scholar
- 33.Raeisi A, Akbarizadeh G, Mahmoudi A (2018) Combined method of an efficient cuckoo search algorithm and nonnegative matrix factorization of different zernike moment features for discrimination between oil spills and lookalikes in SAR images. IEEE J Sel Top Appl Earth Observ Remote Sens 99:1–13Google Scholar
- 34.Sharifzadeh F, Akbarizadeh G, Kavian YS (2018) Ship classification in SAR images using a new hybrid CNN–MLP classifier. J Indian Soc Remote Sens 6:1–12Google Scholar
- 37.Tai Y, Yang J, Liu X, Xu C (2017) Memnet: a persistent memory network for image restoration. In: Proceedings of the IEEE international conference on computer vision, pp 4539–4547Google Scholar
- 40.Yong X, Jie W, Fei L, Zheng Z (2017) Review of video and image defogging algorithms and related studies on image restoration and enhancement. IEEE Access 4:165–188Google Scholar
- 41.Yuan Q, Zhang Q, Li J, Shen H, Zhang L (2018) Hyperspectral image denoising employing a spatial–spectral deep residual convolutional neural network. IEEE Trans Geosci Remote Sens 99:1–14Google Scholar
- 43.Zhang K, Zuo W, Gu S, Zhang L (2017) Learning deep CNN denoiser prior for image restoration. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3929–3938Google Scholar
- 47.Zhou D (2015) Image restoration technology based on discrete neural network. In: MATEC web of conferences, EDP sciences, vol 25, p 03017Google Scholar