Video super-resolution for dual-mode digital cameras via scene-matched learning

Abstract

Many consumer digital cameras support dual shooting mode of both low-resolution (LR) video and high-resolution (HR) image. By periodically switching between the video and image modes, this type of cameras make it possible to super-resolve the LR video with the assistance of neighboring HR still images. We propose a model-based video super-resolution (VSR) technique for the above dual-mode cameras. A HR video frame is modeled as a 2D piecewise autoregressive (PAR) process. The PAR model parameters are learnt from the HR still images inserted between LR video frames. By registering the LR video frames and the HR still images, we base the learning on sample statistics that matches the scene to be constructed. The resulting PAR model is more accurate and robust than if the model parameters are estimated from the LR video frames without referring to the HR images or from a training set. Aided by the powerful scene-matched model the LR video frame is upsampled to the resolution of the HR image via adaptive interpolation. As such, the proposed VSR technique does not require explicit motion estimation of subpixel precision nor the solution of a large-scale inverse problem. The new VSR technique is competitive in visual quality against existing techniques with a fraction of the computational cost.