A Robust Reconstruction-Based Multi-Frame Super-Resolution Method using Optical Flow

• Main Authors: Chih-Yang Chen, 陳智暘
• Other Authors: 莊永裕
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/93799419390309879142

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 103 === Method of integrating a high-resolution (HR) image from multiple observed low-resolution (LR) images is called multi-frame super-resolution (SR). There are basically two stages of reconstruction-based SR: registration of LR images and reconstruction of HR image. In this thesis, we based on the assumption of intensity consistency, and tried several optical flow methods as registration method. Also, based on another assumption: ”forward-backward flow consistency”, we calculated the confidence of a flow, then brought confidence into HR image reconstruction to reduce the error caused by mis-registration. But in the test sets like ”resolution chart”, there will be some errors caused by some patterns with frequencies that is too high. The errors violates the assumption of intensity consistency, which will cause fail registration of optical flow method. Thus, we proposed to applying blur before calculating the flow. The method can prevent optical flow from failing. Also, due to the resolution of images nowadays becomes higher and higher, which will make the reconstruction of HR image need enormous amount of memory usage and time. The thesis proposed to divide the reconstruction to multiple parallelable data blocks to reduce memory and time usage, and proposed multi-thread and GPU speed-ups. As for algorithm speed-up, we proposed combining nearest neighbors (NN) reconstruction and linear reconstruction to achieve acceleration. With the method proposed by this thesis, we can make using optical flow in multi-frame reconstruction-based SR more robust, and reduce the reconstruction time and peak memory usage.