Ruzic, Tijana

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Ghent University Academic Bibliography

Single-Image Example-Based Super-ResolutionUsing Markov Random FieldsTijana Ruzˇic´Supervisor(s): Aleksandra Pizˇurica, Wilfried PhilipsI. INTRODUCTIONSuper-resolution (SR) plays an importantrole in image processing applications nowa-days due to the huge amount of low qual-ity video and image material. Low qualityis a consequence of using low-cost imagingsensors for image/video acquisition, such asweb-cameras, cell phone and surveillance cam-eras. Furthermore, the increasing popularityof HDTV makes the SR methods necessaryfor resolution enhancement of NTSC and PALrecordings. The task of SR is to infer a HRimage from one or more low resolution (LR)images. There are two main approaches: clas-sical and example-based.II. PROPOSED METHOD AND RESULTSIn this paper, we present a new example-based SR method that exploits self-similarityacross different resolution scales of the inputimage. Missing high frequencies are filled inby searching for highly similar patches in thedatabase that contains high-resolution informa-tion. In our case, the database is the image it-self, like in [2], while in the original approachfrom [1] an external database of images is used.The main benefits in comparison with [1] arefaster search and absence of ”hallucination” ef-fect. Compared with [2], we avoid using clas-sical approach on top of example-based onesince it can cause additional errors due to sub-pixel accuracy. Specifically, we search for—————————————————–T. Ruzˇic´ is with the Department of Telecom-munications and Information Processing (TELIN),Ghent University (UGent), Gent, Belgium. E-mail:truzic@telin.ugent.bek nearest neighbours of each input LR patchwithin the Gaussian pyramid of the input LRimage. The corresponding HR patches (fromhigher pyramid level) of those nearest neigh-bours are copied to the corresponding loca-tions in the HR image by combining them ina globally consistent manner. This is achievedby Markov Random Field modelling, as origi-nally suggested in [1]. Therefore, our approachcombines the search method from [2] and thereconstruction method from [1], improving onboth of them at the same time. Fig. 1 demon-strates better performance than reference tech-nique from [2] and standard bi-cubic interpola-tion for magnification factor 4.Figure 1. From left to right: bi-cubic interpolation,[2] and the proposed methodIII. CONCLUSIONSWe presented a novel method for singleimage super-resolution that improves on twomethods upon which it was built. We demon-strated its potential with result.REFERENCES[1] Freeman, W.T., Pasztor, E.C., Carmichael, O.T.:Learning Low-Level Vision. IJCV, vol. 40, no. 1,24–47 (2000)[2] Glasner, D., Bagon, S., Irani, M.: Super-Resolution from a Single Image. In: ICCV(2009)

Single-image example-based super-resolution using Markov random fields

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Single-image example-based super-resolution using Markov random fields

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Ghent University Academic Bibliography