A survey on super-resolution imaging

Tian, Jing; Ma, Kai-Kuang

doi:10.1007/s11760-010-0204-6

Cited by 254 publications

(103 citation statements)

References 110 publications

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“…First, one can increase the pixel numbers, but at the cost of lower signalto-noise ratio (SNR) and/or longer acquisition time. 6 Second, the chip size can be increased; however, the chip size necessary to capture a very HR image would be very expensive. 6 An interesting alternative to all these options is to use an image processing method called super-resolution (SR).…”

Section: Introductionmentioning

confidence: 99%

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Comparison of super-resolution algorithms applied to retinal images

et al. 2014

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Abstract. A critical challenge in biomedical imaging is to optimally balance the trade-off among image resolution, signal-to-noise ratio, and acquisition time. Acquiring a high-resolution image is possible; however, it is either expensive or time consuming or both. Resolution is also limited by the physical properties of the imaging device, such as the nature and size of the input source radiation and the optics of the device. Super-resolution (SR), which is an off-line approach for improving the resolution of an image, is free of these trade-offs. Several methodologies, such as interpolation, frequency domain, regularization, and learning-based approaches, have been developed over the past several years for SR of natural images. We review some of these methods and demonstrate the positive impact expected from SR of retinal images and investigate the performance of various SR techniques. We use a fundus image as an example for simulations.

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Section: Introductionmentioning

confidence: 99%

“…6 Second, the chip size can be increased; however, the chip size necessary to capture a very HR image would be very expensive. 6 An interesting alternative to all these options is to use an image processing method called super-resolution (SR). 7 SR imaging was first introduced by Tsai and Huang 8 in 1984.…”

Section: Introductionmentioning

confidence: 99%

Comparison of super-resolution algorithms applied to retinal images

et al. 2014

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“…Unfortunately, it is very difficult to estimate (even manually) the contribution of such particles to the measured size distribution because of their small size in comparison to much larger particles that might be nearby. The possible solution of the problem may be found by the incorporation of superresolution image processing methods [44]. This is being considered for future versions of the algorithm.…”

Section: Comparison Of Volume Distributions For Different Magnificationsmentioning

confidence: 99%

Application of the Hough transform for the automatic determination of soot aggregate morphology

et al. 2012

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We report a new method for automated identification and measurement of primary particles within soot aggregates as well as the sizes of the aggregates and discuss its application to high-resolution transmission electron microscope (TEM) images of the aggregates. The image processing algorithm is based on an optimized Hough transform, applied to the external border of the aggregate. This achieves a significant data reduction by decomposing the particle border into fragments, which are assumed to be spheres in the present application, consistent with the known morphology of soot aggregates. Unlike traditional techniques, which are ultimately reliant on manual (human) measurement of a small sample of primary particles from a subset of aggregates, this method gives a direct measurement of the sizes of the aggregates and the size distributions of the primary particles of which they are composed. The current version of the algorithm allows processing of high-resolution TEM images by a conventional laptop computer at a rate of 1-2 ms per aggregate. The results were validated by comparison with manual image processing, and excellent agreement was found.

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“…Super-resolution: Many algorithms have been proposed in super-resolution literature [9,12,17,26]. The reconstruction based algorithm is a popular technique that performs subpixel registration of multiple low-resolution images, followed by iterative optimization to estimate a high-resolution image [18,22].…”

Section: Related Workmentioning

confidence: 99%

Super-Resolution from Corneal Images

Nitschke

Nakazawa

2012

Procedings of the British Machine Vision Conference 2012

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The corneal imaging technique enables extraction of scene information from corneal reflections and realizes a large number of applications including environment map reconstruction and estimation of a person's area of view. However, since corneal reflection images are usually low quality and resolution, the outcome of the technique is currently limited. To overcome this issue, we propose a first non-central catadioptric approach to reconstruct high-resolution scene information from a series of lower resolution corneal images through a super-resolution technique. We describe a three-step process, including (1) single image environment map recovery, (2) multiple image registration, and (3) high-resolution image reconstruction. In a number of experiments we show that the proposed strategy successfully recovers high-frequency textures that are lost in the source images, and also works with other non-central catadioptric systems, e.g., involving spherical mirrors. The obtained information about a person and the environment enables novel applications, e.g., for surveillance systems, personal video, human-computer interaction, and upcoming head-mounted cameras (Google Glass [5]).

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A survey on super-resolution imaging

Cited by 254 publications

References 110 publications

Comparison of super-resolution algorithms applied to retinal images

Comparison of super-resolution algorithms applied to retinal images

Application of the Hough transform for the automatic determination of soot aggregate morphology

Super-Resolution from Corneal Images

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