並列摘要
Super resolution is developed to enhance the resolution of images and various kinds of learning based methods were proposed to magnify a single image. This paper presents a 2D hidden Markov model which could do super resolution by using learned image patch pair database. The image patch pairs store the correspondence relation of high-frequency information between low resolution (LR) patches and high resolution (HR) patches. For each input LR patch, the top five similar LR candidate patches in database are searched to construct a 3D cube which can then be modeled by the proposed 2D hidden Markov model (HMM). A novel 2D Viterbi algorithm is developed to find the optimal LR candidate patches that are the most compatible with each other. The resulting super resolution image could be formed by pasting back the corresponding HR patches from patch pair database according to the positions of found optimal LR patches. By objective comparisons of PSNRs/SSIMs and subjective judgment of the generated super resolution images, the proposed 2D HMM method is superior to the traditional interpolation methods and some existing state-of-the-art methods.
延伸閱讀
- 全柏翰（2010）。Patch-Based Video Super-Resolution Using 2-D Hidden Markov Model〔碩士論文，大同大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0081-3001201315104937
- 張曉維、楊富文、林慧珍、施定為（2016）。Clustered Example Based Super Resolution。中華科技大學學報，(65)，33-49。https://www.airitilibrary.com/Article/Detail?DocID=a0000585-201601-201602150007-201602150007-33-49
- Chen, H. W. (2012). Image Super Resolution Algorithm Based on Patch Similarity and Geometric Distance [master's thesis, National Tsing Hua University]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0016-2002201315380120