Super-resolution of facial images in forensics scenarios

Forensics facial images are usually provided by surveillance cameras and are therefore of poor quality and resolution. Simple upsampling algorithms can not produce artifact-free higher resolution images from such low-resolution (LR) images. To deal with that, reconstruction-based super-resolution (SR) algorithms might be used. But, the problem with these algorithms is that they mostly require motion estimation between LR and low-quality images which is not always practical. To deal with this, we first simply interpolate the LR input images and then perform motion estimation. The estimated motion parameters are then used in a non-local mean-based SR algorithm to produce a higher quality image. This image is further fused with the interpolated version of the reference image via an alpha-blending approach. The experimental results on benchmark datasets and locally collected videos from surveillance cameras, show the outperformance of the proposed system over similar ones.