An improved multi-frame super-resolution technique

Abstract

We propose an improvement on the existing super-resolution technique that produces high resolution video from low quality low resolution video. Our method has two steps: (1) motion registration and (2) regularization using back-projection. Sub-pixel motion parameters are estimated for a group of 16 low resolution frames with reference to the next frame and these are used to position the low resolution pixels on high resolution grid. A gradient based technique is used to register the frames at the sub-pixel level. Once we get the high resolution grid, we use an improved state-of-the-art regularization technique where the image is iteratively modified by applying back-projection to get a sharp and undistorted image. This technique is based on bilateral prior and deals with different data and noise models. This computationally inexpensive method is robust to errors in motion/blur estimation and results in images with sharp edges. The proposed system is faster than the existing ones as the post-processing steps involved only simple filtering. The results show the proposed method gives high quality and high resolution videos and minimizes effects due to camera jerks. This technique can easily be ported to hardware and can be developed into a product.