Super-resolution for simultaneous realization of resolution enhancement and motion blur removal based on adaptive prior
- PDF / 4,515,276 Bytes
- 17 Pages / 595 x 794 pts Page_size
- 30 Downloads / 211 Views
R ESEA R CH
Open Access
Super-resolution for simultaneous realization of resolution enhancement and motion blur removal based on adaptive prior settings Takahiro Ogawa* , Daisuke Izumi, Akane Yoshizaki and Miki Haseyama
Abstract A super-resolution method for simultaneously realizing resolution enhancement and motion blur removal based on adaptive prior settings are presented in this article. In order to obtain high-resolution (HR) video sequences from motion-blurred low-resolution video sequences, both of the resolution enhancement and the motion blur removal have to be performed. However, if one is performed after the other, errors in the first process may cause performance deterioration of the subsequent process. Therefore, in the proposed method, a new problem, which simultaneously performs the resolution enhancement and the motion blur removal, is derived. Specifically, a maximum a posterior estimation problem which estimates original HR frames with motion blur kernels is introduced into our method. Furthermore, in order to obtain the posterior probability based on Bayes’ rule, a prior probability of the original HR frame, whose distribution can adaptively be set for each area, is newly defined. By adaptively setting the distribution of the prior probability, preservation of the sharpness in edge regions and suppression of the ringing artifacts in smooth regions are realized. Consequently, based on these novel approaches, the proposed method can perform successful reconstruction of the HR frames. Experimental results show impressive improvements of the proposed method over previously reported methods. 1 Introduction High-resolution (HR) video sequences are necessary for various fundamental applications, and acquisition of data with an HR image sensor makes quality improvement straightforwardly. However, it is often difficult to capture video sequences with sufficient high quality from current image sensors. Furthermore, video sequences often include motion blurs in many situations, e.g., there is not enough light to avoid the use of a long shutter speed. Then image processing methodologies for increasing the visual quality are necessary to bridge the gap between demands of applications and physical constraints. Many researchers have proposed super-resolution (SR) methods for increasing the resolution levels of low-resolution (LR) video sequences [1-30]. Most SR methods are broadly categorized into two approaches, learning-based (examplebased) approach and reconstruction-based approach. The learning-based approach estimates the HR frame from *Correspondence: [email protected] Graduate School of Information Science and Technology, Hokkaido University, Sapporo 060-0814, Japan
only its LR frame, but several other HR frames are utilized to learn a prior on the original HR frame [1-9]. On the other hand, the reconstruction-based approach estimates the HR frame from their multiple LR frames, and many methods based on this approach have been proposed [1030]. In this article, we focus on the reconstruction-based
Data Loading...
