Super Resolution With Probabilistic Motion Estimation

Super-resolution reconstruction (SRR) has long been relying on very accurate motion estimation between the frames for a successful process. However, recent works propose SRR that bypasses the need for an explicit motion estimation [11], [15]. In this correspondence, we present a new framework that u...

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Bibliographic Details
Published in:IEEE transactions on image processing 2009-08, Vol.18 (8), p.1899-1904
Main Authors: Protter, M., Elad, M.
Format: Article
Language:English
Subjects:
Additive noise
Applied sciences
Deinterlacing
Differential equations
Exact sciences and technology
Gaussian noise
Image processing
Image reconstruction
Image resolution
Information, signal and communications theory
Miscellaneous
Motion estimation
Noise generators
Noise measurement
probabilistic motion estimation
Robustness
Sampling, quantization
Signal and communications theory
Signal processing
Solid modeling
super resolution
Telecommunications and information theory
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Summary:Super-resolution reconstruction (SRR) has long been relying on very accurate motion estimation between the frames for a successful process. However, recent works propose SRR that bypasses the need for an explicit motion estimation [11], [15]. In this correspondence, we present a new framework that ultimately leads to the same algorithm as in our prior work [11]. The contribution of this paper is two-fold. First, the suggested approach is much simpler and more intuitive, relying on the classic SRR formulation, and using a probabilistic and crude motion estimation. Second, the new approach offers various extensions not covered in our previous work, such as more general re-sampling tasks (e.g., de-interlacing).
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2009.2022440