No-reference image quality assessment for horizontal-path imaging scenarios

Ríos, Carlos Eduardo Uc; Gładysz, Szymon

doi:10.1117/12.2015710

Cited by 6 publications

(6 citation statements)

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“…Other metrics include (image) sharpness, defined as the (normalized) square root of the sum of squared (grey value) gradients in horizontal and vertical direction, edge-based measures, such as the sum or variance of edges in the image, Fisher information, Fourier spectral analysis, total variation, Shannon entropy, etc. A short overview of the most common of these methods is given in [9] and [10].…”

Section: "No Reference" Metricsmentioning

confidence: 99%

An appraisal of suitable evaluation methods for turbulence mitigation algorithms

Hübner

2022

Environmental Effects on Light Propagation and Adaptive Systems V

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In order to mitigate atmospheric turbulence effects such as increased blur, reduced contrast, and image motion as well as geometric deformations, a wide variety of reconstruction techniques has been developed. Such techniques have proven reasonably successful in mitigating one or several turbulence effects but frequently at the cost of introducing unwanted artefacts such as ringing or noise amplification, depending on the algorithm's properties. The application of image quality metrics (IQMs) as a means of comparing the results of various reconstruction algorithms as objectively as possible is a widely used practice. However, added noise and artefacts affect IQMs which rely on information like high frequency components, disproportionately, since noisy results are invariably interpreted as "higher quality". The underlying goal of this article is to define a methodology for comparing the performance of structurally differing algorithms by a combination of select quality metrics. As different metrics will likely yield different ratings for the same algorithm's performance, a combination of suitable metrics is proposed. Therefore the main focus here is foremost on a survey of current methods for assessing image quality in general and on appraising their suitability for evaluating the quality of images processed by turbulence mitigation algorithms in particular.

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Section: "No Reference" Metricsmentioning

confidence: 99%

An appraisal of suitable evaluation methods for turbulence mitigation algorithms

Hübner

2022

Environmental Effects on Light Propagation and Adaptive Systems V

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“…In order to eliminate edge effects influencing the Fourier transforms, the GLF and the Heaviside function are each mirrored such that the ends of each distribution are a minimum, and then the entire distribution is reduced by this minimum. Wiener Deconvolution [4] is used to extract the LSF from these two distributions, and then the FWHM is extracted from the LSF. Results of this method are shown in Figure 5 through Figure 14.…”

Section: Figure 4 -Observed Seasat Satellite Data With a Line Drawn Pmentioning

confidence: 99%

“…The Strehl ratio is the traditional measure of AO performance, but it has several shortcomings when applied to HAO systems: it requires a point source target, which may not be available in scenarios of interest; the image processing stage can oversharpen, leading to exaggerated scores; and under some circumstances the Strehl ratio is not a reliable predictor of image quality [3]. Other no-reference metrics tend to have poor sensitivity when applied to the types of iterative deconvolution algorithms that are commonly used in HAO systems [4].…”

Section: Introductionmentioning

confidence: 99%

A new performance metric for hybrid adaptive optics systems

Werth

Bos

Calef

et al. 2014

2014 IEEE Aerospace Conference

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Advances in Adaptive Optics (AO) systems and image processing techniques have allowed image reconstruction to reach new levels of sophistication with impressive improvements in resolvability. The products of these reconstruction methods are often compared by eye due to a lack of widely agreed-upon objective performance metrics. This paper describes a new technique for assessing the performance of image reconstruction algorithms and AO systems. A line spread function is extracted from an object's edges, and the full width at half maximum of this experimentally measured line spread function is compared to the full width at half maximum of the system's diffraction-limited line spread function. This technique is used with imagery of satellites taken with the 3.6m Maui AEOS telescope with and without an AO system and with image post-processing. The AO system in question allows for rapid switching between compensated and uncompensated imagery, so an objective comparison between the two is possible.

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“…The advanced image post-processing techniques like blind deconvolution 2,3 and deconvolution from wavefront sensing 4,5 are still needed for approaching the diffraction-limited resolution. However, some important tasks in the post-processing procedure, including frame selection, stopping iteration and algorithm comparison 6 , are commonly judged by the subjective evaluation of images quality due to a lack of widely agreed-upon objective quality metrics. The term objective means that a quality index can automatically assess the perpetual image quality consistently with the assessment results by human visual system (HVS), and then this metric can be used to automatically select the least-degraded image frames in the short-exposure image sequences 1 and compare the performance of different deconvolution algorithms 6 .…”

Section: Introductionmentioning

confidence: 99%

“…However, some important tasks in the post-processing procedure, including frame selection, stopping iteration and algorithm comparison 6 , are commonly judged by the subjective evaluation of images quality due to a lack of widely agreed-upon objective quality metrics. The term objective means that a quality index can automatically assess the perpetual image quality consistently with the assessment results by human visual system (HVS), and then this metric can be used to automatically select the least-degraded image frames in the short-exposure image sequences 1 and compare the performance of different deconvolution algorithms 6 . Some interesting computational models 7 have been presented for both full-reference and no-reference image quality assessment (IQA) 8 in the field of image processing in recent years.…”

Section: Introductionmentioning

confidence: 99%

Perceptual image quality in normalized LOG domain for Adaptive Optics image post-processing

Guo

Zhang²,

et al. 2015

SPIE Proceedings

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Adaptive Optics together with subsequent post-processing techniques obviously improve the resolution of turbulencedegraded images in ground-based space objects detection and identification. The most common method for frame selection and stopping iteration in post-processing has always been subjective viewing of the images due to a lack of widely agreed-upon objective quality metric. Full reference metrics are not applicable for assessing the field data, noreference metrics tend to perform poor sensitivity for Adaptive Optics images. In the present work, based on the Laplacian of Gaussian (LOG) local contrast feature, a nonlinear normalization is applied to transform the input image into a normalized LOG domain; a quantitative index is then extracted in this domain to assess the perceptual image quality. Experiments show this no-reference quality index is highly consistent with the subjective evaluation of input images for different blur degree and different iteration number.

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No-reference image quality assessment for horizontal-path imaging scenarios

Cited by 6 publications

References 0 publications

An appraisal of suitable evaluation methods for turbulence mitigation algorithms

An appraisal of suitable evaluation methods for turbulence mitigation algorithms

A new performance metric for hybrid adaptive optics systems

Perceptual image quality in normalized LOG domain for Adaptive Optics image post-processing

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