- Media data analysis
Deep learning image quality improvement
Applications
- Improve quality of images from on-board and security cameras.
- Improve quality of images from various image sensors.
Benchmarks, strengths, and track record
- In the case of images with unexpected noise level outside of the scope of prior learning, improved restoration error to less than one third of conventional deep learning noise removal technologies.
Inquiries
Please include the title “Toshiba AI Technology Catalog: Deep learning image quality improvement” or the URL in the inquiry text.
Please note that because this technology is currently the subject of R&D activities, immediate responses to inquiries may not be possible.
References:
- K. Isogawa, et al., ”Deep Shrinkage Convolutional Neural Network for Adaptive Noise Reduction,” IEEE Signal Processing Letters, Vol. 25, No.2, Feb. 2018.
- Toshiba Review; Vol.71, No.3, p.41 (March 2016) (PDF) (in Japanese)
- SSII2018 Symposium
- ISMRM2018 Conference
- ICIP2018 Conference