Hardware-accelerated Video Fusion

This projects aim at producing a low-power demonstrator for real-time video fusion using a hybrid SoC device that combines a low-power Cortex A9 multi-core processor and a FPGA fabric. The methodology involves using a fusion algorithm developed at Bristol based on Complex dual-tree wavelet transforms.  These transforms work in forward and inverse mode together with configurable fusion rules to offer high quality fusion output.

The complex dual-tree wavelet transforms represents  around 70% of total complexity. The wavelet accelerator designed at Bristol removes this complexity and accelerates the whole application by a factor of x4.  It also has a significant positive impact in overall energy. There is a negligible increase in power due to the fact that the fabric works in parallel with the main processor. Notice that if the optimization criteria is not performance or energy but power then the processor and fabric could reduce its clock frequency and voltage and obtain a significant reduction in power for the same energy and performance levels.

This project has built a system extended with frame capturing capabilities using thermal and visible light cameras.  In this link you can see the system working in our labs : hardware accelerated video fusion

This project has been funded by the Technology Strategy Board under their energy-efficient computers program with Qioptiq Ltd as industrial collaborator.

This research will be presented and demonstrated at FPL 2015, London in September.

Video super-resolution

Motion compensated video super-resolution is a technique that uses the sub-pixel shifts between multiple low resolution images of the same scene to create higher resolution frames with improved quality. An important concept is that due to the sub-pixel displacements of picture elements in the low resolution frames, it is possible to obtain high frequency content beyond the Nyquist limit of the sampling equipment. Super-resolution algorithms exploit the fact that as objects move in front of the camera sensor, picture elements captured in the camera pixels might not be visible in the next frame if the movement of the element does not extend to the next pixel. Super-resolution algorithms track and position these additional picture elements in the high-resolution frame. The resulting video quality is significantly improved compared with techniques that only exploit the information in one low-resolution frame to create one high resolution frame.

Super-Resolution techniques can be applied to many areas, including intelligent personal identification, medical imaging, security, surveillance and can be of special interest in applications that demand low-power and low-cost sensors. The key idea is that increasing the pixel size improves the signal to noise ratio and reduces the cost and power of the sensor.  Larger pixels enable more light to be collected and in addition the blur introduced by diffraction is reduced. Diffraction is a bigger issue with smaller pixels, so again sensors with larger pixels will perform better, giving sharper images with higher contrast in the fine details, especially in low-light conditions.

Benefits include that increasing the pixel size means that fewer pixels can be located in the sensor and this reduces the sensor resolution.  The low-resolution sensor needs to process and transmit a lower amount of information which results in lower power and cost.  Super-resolution algorithms running in the receiver side can then be used to recover high-quality and high-resolution videos maintaining a constant frame rate.

Overall, super-resolution enables the system that captures and transmits the video data to be based on low-power and low-cost components while the receiver still obtains a high-quality video stream.

This project has been sponsored by the Centre for Defence Enterprise and DSTL under the Generic Enablers for Low-Size, Weight, Power and Cost (SWAPC) Intelligence, Surveillance, Target Acquisition and Reconnaissance (ISTAR) program.

Click to see some examples :

1:  before  car number plate in and after super-resolution car number plate SR

2:  before vehicles in and after super-resolution vehicles SR

and learn about the theory behind the algorithm:  Chen, J, Nunez-Yanez, JL & Achim, A 2014, ‘Bayesian video super-resolution with heavy-tailed prior models’. IEEE Transactions on Circuits and Systems for Video Technology, vol 24., pp. 905-914