Adaptive regularized image interpolation using data fusion and steerable constraints Jeong-Ho Shin a , Joon-Ki Paik a , Jeffery R. Price b , and Mongi A. Abidi b a Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University 221 Huksuk-Dong, Tongjak-Ku, Seoul 156-756, Korea b Department of Electrical and Computer Engineering, University of Tennessee, Knoxville, TN, USA ABSTRACT This paper presents an adaptive regularized image interpolation algorithm from blurred and noisy low resolution image sequence, which is developed in a general framework based on data fusion. This framework can preserve the high frequency components along the edge orientation in a restored high resolution image frame. This multiframe image interpolation algorithm is composed of two levels of fusion algorithm. One is to obtain enhanced low resolution images as an input data of the adaptive regularized image interpolation based on data fusion. The other one is to construct the adaptive fusion algorithm based on regularized image interpolation using steerable orientation analysis. In order to apply the regularization approach to the interpolation procedure, we first present an observation model of low resolution video formation system. Based on the observation model, we can have an interpolated image which minimizes both residual between the high resolution and the interpolated images with a prior constraints. In addition, by combining spatially adaptive constraints, directional high frequency components are preserved with efficiently suppressed noise. In the experimental results, interpolated images using the conventional algorithms are shown to compare the conventional algorithms with the proposed adaptive fusion based algorithm. Experimental results show that the proposed algorithm has the advantage of preserving directional high frequency components and suppressing undesirable artifacts such as noise. Keywords: Image interpolation, data fusion, steerable filter, regularization, resolution enhancement, adaptive edge preserving 1. INTRODUCTION High resolution (HR) restoration has many applications in image processing. There are two categories in high resolution restoration. One is the traditional image restoration concerned with the reconstruction of an uncorrupted image from a blurred and noisy one. 1 The other one is the image interpolation associated with increase of the spatial resolution of a single or a set of image frames. 2,3 HR image processing applications such as digital high-definition television (HDTV), aerial photo, medical imaging, surveillance video and military purpose images, need HR image interpolation algorithms. In this paper, we mainly deal with image interpolation algorithms to enhance the image quality in the sense of resolution. By introducing image fusion and adaptive regularization algorithms, the proposed algorithm can restore HR image from low-resolution (LR) video. Originally, the objective of image fusion is to combine information from multiple images of the same scene. As a result of image fusion, a single image which is more suitable for human and machine perception or further image-processing tasks can be obtained. 4 Data fusion algorithms are usually used in applications ranging from Earth resource monitoring, weather forecasting, and vehicular traffic control to military target classification and tracking. 5 By utilizing the nature of image fusion mentioned above, we can not only make Further author information: (Send correspondence to J. K. Paik) J. H. Shin : E-mail: shinj@ms.cau.ac.kr J. K. Paik : E-mail: paikj@cau.ac.kr J. R. Price :E-mail: jrp@utk.edu M. A. Abidi : E-mail: abidi@utk.edu