Hybrid super-resolution combining example-based single-image and interpolation-based multi-image reconstruction approaches

Achieving a higher spatial resolution is of particular interest in many applications such as video surveillance and can be realized by employing higher resolution sensors or applying super-resolution methods. Traditional super-resolution algorithms are based on either a single low resolution image or on multiple low resolution frames. In this paper, a hybrid super-resolution method is proposed which combines both a single-image and a multi-image approach using a soft decision mask. The mask is computed from the motion information utilized in the multi-image super-resolution part. This concept is shown to work for one particular setup but is also extensible toward other combinations of single-image and multi-image super-resolution algorithms as well as other merging metrics. Simulation results show an average luminance PSNR gain of up to 0.85 dB and 0.59 dB for upscaling factors of 2 and 4, respectively. Visual results substantiate the objective results.