Article

Maximum a Posteriori Video Super-Resolution Using a New Multichannel Image Prior

Faculty of Physics, Department of Electronics, Computers, Telecommunications and Control, National and Kapodistrian University of Athens, Panepistimiopolis, Zografos, 15784 Athens, Greece.
IEEE Transactions on Image Processing (Impact Factor: 3.63). 06/2010; 19(6):1451-64. DOI: 10.1109/TIP.2010.2042115
Source: PubMed

ABSTRACT

Super-resolution (SR) is the term used to define the process of estimating a high-resolution (HR) image or a set of HR images from a set of low-resolution (LR) observations. In this paper we propose a class of SR algorithms based on the maximum a posteriori (MAP) framework. These algorithms utilize a new multichannel image prior model, along with the state-of-the-art single channel image prior and observation models. A hierarchical (two-level) Gaussian nonstationary version of the multichannel prior is also defined and utilized within the same framework. Numerical experiments comparing the proposed algorithms among themselves and with other algorithms in the literature, demonstrate the advantages of the adopted multichannel approach.

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    • "However, they are ineffective under blurry and noisy cases. The second type (e.g., maximum a posteriori (MAP) [7], projection onto convex set (POCS) [8], etc.) relies on the accuracy of the required subpixel registration process, which is even more difficult in the superresolution setting. The type later known as single image superresolution is based on machine learning techniques, with the attempt to exceed some of the limitations mentioned above. "
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    • "Protter et al. had come up with a simple super resolution technique by applying denoising method called Nonlocal-Means algorithm (NLM) [11]. Other approaches to the SR problems include Maximum a posteriori (MAP) [12], iterative back-projection [13], POCS [2], [14], [15] and wavelet-based algorithm [16]. Meanwhile, learning-based SR algorithms work by extracting redundant high-frequency image information from training samples which contained known HR components. "
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    • "Iteration back projection (IBP) [3], [4] algorithms estimate the HR image by iteratively back projecting the error between simulated LR images and the observed ones. Maximum a posteriori (MAP) [5]–[7] approaches adopt the prior probability of target HR images to stabilize the solution space under a Bayesian framework. Projection on convex sets (POCS) [8], [9] tends to consider the solution as an element on a convex set defined by the input LR images. "
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