A vector quantizer for image restoration

Sheppard, D.G.; Bilgin, Ali; Nadar, Mariappan S.; Hunt, B. R.; Marcellin, Michael W.

doi:10.1109/83.650857

Cited by 25 publications

(7 citation statements)

References 19 publications

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“…Since the scaling coefficients include only low frecjuency information, it is assumed a Wiener filter restores the information well enough. In practice, the difference between results using this method and the true data is smaJl, as was seen in [12].…”

Section: Overviewmentioning

confidence: 86%

“…In this work. I will follow the concept in [12] and calculate the scaling coefficient values by using a Wiener filter on the blurred image, transforming into the wavelet domain, and then extracting only the scaling coefficient data. Since the scaling coefficients include only low frecjuency information, it is assumed a Wiener filter restores the information well enough.…”

Section: Overviewmentioning

confidence: 99%

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Wavelet Domain Image Restoration and Super-Resolution

Goda¹

2002

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In the unlikely event that the author dkl not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERINGIn Partial Fulfillment of the Reciuirements For the Degree of DOCTOR OF PHILOSOPHY STATEMENT BY AUTHORThis dissertation has been submitted in partial fulfillment of recjuirements for an advanced degree at The University of Arizona and is deposited in the University Library'' to be made available to borrowers under rules of the Library.Brief quotations from this dissertation are allowable without special permission, provided that acciurate acknowledgment of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the Graduate College when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author. An especially deep note of appreciation is due to my wife Tamara for the love and encouragement she provided every step of the way during this program, and also for the past ten years that brought us to this point. Although she may not understand much past this point, she fills my life with joy in a way nothing else can.Finally I want to thank the Creator of all we see and that which I study, and his son, Jesus, who sustains me daily. "It is the glory of God to conceal a matter; to search out a matter is the glory of kings.' Proverbs 25:2 (NIV) It is glorious to ponder the way He has set things in motion and continues to sustain them! 5 LIST OF FIGURESFIGURE 2.1. Model of a general imaging system 18 FIGURE 2.2. 3-D plot of (a) jinc^, the PSF for a circular aperture imaging system and (b) the related MTF 21 FIGURE 2.3. 1-D plot of (a) jinc function, (b) jinc^ fimction, the PSF for a circular aperture imaging system, and (c) the related MTF. Note that the dotted line in (c) is the triangle function (straight line) for comparison. 22 The ability to super-resolve imagery using wavelet-domain techniciues is the second major focus of this research. Super-resolution, the ability to reconstruct object information lost in the imaging process, has been an active research area for many years. Multiple experiments are presented which demonstrate the possibilities and problems associated with super-resolution in the wavelet-domain. Finally, superresolution in the wavelet domain using Non-Linear Interpolative Vector Quantization is studied and the results of the algorithm are presented and discussed.14 CHAPTER 1

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Section: Overviewmentioning

confidence: 86%

Section: Overviewmentioning

confidence: 99%

Wavelet Domain Image Restoration and Super-Resolution

Goda¹

2002

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“…However, finding the analogy from the example images requires a significant amount of computation, and even more to further enhance the performance. For image restoration, Sheppard et al proposed vector quantization (VQ) using the discrete cosine transform (DCT) [14]. The DCT does not effectively consider directionality in the sense that it cannot distinguish between diagonal and anti-diagonal edge components.…”

Section: Introductionmentioning

confidence: 99%

Image interpolation using interpolative classified vector quantization

Hong

Park

Yang

et al. 2008

Image and Vision Computing

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“…Several authors have shown that clustering the data beforehand improves the performance of algorithms which attempt to "learn" features from a small number of labelled examples. [44][45][46][47][48][49] The use of clustering for image restoration has also been explored in some detail; see Sheppard et al 50 for a recent review. For remote detection and characterization of gaseous plumes, the ground scene ceases to be the signal of interest, and becomes instead the clutter.…”

Section: Clustering and Segmentationmentioning

confidence: 99%

Co-design of software and hardware to implement remote sensing algorithms

et al. 2002

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Both for offline searches through large data archives and for onboard computation at the sensor head, there is a growing need for ever-more rapid processing of remote sensing data. For many algorithms of use in remote sensing, the bulk of the processing takes place in an "inner loop" with a large number of simple operations. For these algorithms, dramatic speedups can often be obtained with specialized hardware. The difficulty and expense of digital design continues to limit applicability of this approach, but the development of new design tools is making this approach more feasible, and some notable successes have been reported. On the other hand, it is often the case that processing can also be accelerated by adopting a more sophisticated algorithm design. Unfortunately, a more sophisticated algorithm is much harder to implement in hardware, so these approaches are often at odds with each other. With careful planning, however, it is sometimes possible to combine software and hardware design in such a way that each complements the other, and the final implementation achieves speedup that would not have been possible with a hardware-only or a software-only solution. We will in particular discuss the co-design of software and hardware to achieve substantial speedup of algorithms for multispectral image segmentation and for endmember identification.

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A vector quantizer for image restoration

Cited by 25 publications

References 19 publications

Wavelet Domain Image Restoration and Super-Resolution

Wavelet Domain Image Restoration and Super-Resolution

Image interpolation using interpolative classified vector quantization

Co-design of software and hardware to implement remote sensing algorithms

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