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Subpixel rendering

About: Subpixel rendering is a research topic. Over the lifetime, 3885 publications have been published within this topic receiving 82789 citations.


Papers
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Proceedings ArticleDOI
23 Mar 1992
TL;DR: A new two-step procedure is proposed, and it is shown that the POCS formulation presented for the high-resolution image reconstruction problem can also be used as a new method for the restoration of spatially invariant blurred images.
Abstract: The authors address the problem of reconstruction of a high-resolution image from a number of lower-resolution (possibly noisy) frames of the same scene where the successive frames are uniformly based versions of each other at subpixel displacements. In particular, two previously proposed methods, a frequency-domain method and a method based on projections onto convex sets (POCSs), are extended to take into account the presence of both sensor blurring and observation noise. A new two-step procedure is proposed, and it is shown that the POCS formulation presented for the high-resolution image reconstruction problem can also be used as a new method for the restoration of spatially invariant blurred images. Some simulation results are provided. >

377 citations

Journal ArticleDOI
TL;DR: Experimental data presented here show that the new algorithm yields superior registration precision in the presence of aliasing when compared to several earlier methods and has comparable precision to the iterative method of P. Thevenaz et al. (1998).
Abstract: This paper presents a new direct Fourier-based algorithm for performing image-to-image registration to subpixel accuracy, where the image differences are restricted to translations and uniform changes of illumination. The algorithm detects the Fourier components that have become unreliable estimators of shift due to aliasing, and removes them from the shift-estimate computation. In the presence of aliasing, the average precision of the registration is a few hundredths of a pixel. Experimental data presented here show that the new algorithm yields superior registration precision in the presence of aliasing when compared to several earlier methods and has comparable precision to the iterative method of P. Thevenaz et al. (1998).

365 citations

Journal ArticleDOI
TL;DR: It is shown that the edge location is related to the so-called ``Christoffel numbers'' and is compared with Sobel and Hueckel edge detectors in presence and absence of noise.
Abstract: A new method for locating edges in digital data to subpixel values and which is invariant to additive and multiplicative changes in the data is presented For one-dimensional edge patterns an ideal edge is fit to the data by matching moments It is shown that the edge location is related to the so-called ``Christoffel numbers'' Also presented is the study of the effect of additive noise on edge location The method is extended to include two-dimensional edge patterns where a line equation is derived to locate an edge This in turn is compared with the standard Hueckel edge operator An application of the new edge operator as an edge detector is also provided and is compared with Sobel and Hueckel edge detectors in presence and absence of noise

359 citations

Journal ArticleDOI
TL;DR: In this paper, the problem of subpixel spectral detection of targets in remote sensing images is considered, where two constrained target detection approaches are studied and compared and some suggestions are further proposed to mitigate their disadvantages.
Abstract: Target detection in remotely sensed images can be conducted spatially, spectrally or both. The difficulty of detecting targets in remotely sensed images with spatial image analysis arises from the fact that the ground sampling distance is generally larger than the size of targets of interest in which case targets are embedded in a single pixel and cannot be detected spatially. Under this circumstance target detection must be carried out at subpixel level and spectral analysis offers a valuable alternative. In this paper, the problem of subpixel spectral detection of targets in remote sensing images is considered, where two constrained target detection approaches are studied and compared. One is a target abundance-constrained approach, referred to as nonnegatively constrained least squares (NCLS) method. It is a constrained least squares spectral mixture analysis method which implements a nonnegativity constraint on the abundance fractions of targets of interest. Another is a target signature-constrained approach, called constrained energy minimization (CEM) method. It constrains the desired target signature with a specific gain while minimizing effects caused by other unknown signatures. A quantitative study is conducted to analyze the advantages and disadvantages of both methods. Some suggestions are further proposed to mitigate their disadvantages.

350 citations

Journal ArticleDOI
TL;DR: It is shown that the procedure achieves the claims of super resolution schemes but, unlike many of them, is well posed and robust.

332 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202387
2022209
2021120
2020179
2019189
2018263