Real-time Dictionary based Super-Resolution of Surveillance Video Streams and Targets

Real-time super-resolution within surveillance video streams is a powerful tool for security and crime prevention allowing for example, events, faces or objects such number-plates and luggage to be more accurately identified on the fly and from a distance. However, many of the state of the art approaches to super-resolution are computationally too expensive to be suitable for real-time applications within a surveillance context. We consider one particular contemporary method based on sparse coding, 1 and show how, by relaxing some model constraints, it can be sped up significantly compared to the reference implementation, and thus approach real-time performance with visually indistinct reduction in fidelity. The final computation is three orders of magnitude faster than the reference implementation. The quality of the output is maintained: PSNR of the super-resolved images compared to ground truth is not significantly different to the reference implementation, while maintaining a noticeable improvement over baseline bicubic-interpolation approach.