A new performance metric for hybrid adaptive optics systems

Werth, M.; Bos, Jeremy P.; Calef, Brandoch; Williams, S. D.; Thompson, Daniel W.; Williams, Skip

doi:10.1109/aero.2014.6836192

Cited by 10 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The edge variance calculates an edge map and the variance of this edge map is used as a metric for image quality. 10 Fisher information, 11 on the other hand, measures the disorder in an image. Total variation is also a very popular method based on images gradients.…”

Section: Methodsmentioning

confidence: 99%

The EDA TURBO project: software-based atmospheric turbulence mitigation

Hofmann

Goelzer²,

Gładysz³

2022

Environmental Effects on Light Propagation and Adaptive Systems V

View full text Add to dashboard Cite

As European optronic industries are facing an increasing demand for high-definition imagery, atmospheric turbulence becomes one of the main factors hindering the performance of military equipment used for intelligence, surveillance and reconnaissance. The TURBO project, which is funded by European Defence Agency and has Fraunhofer IOSB, TNO and Adimec as partners, aims to develop a real-time demonstrator capable of selecting the optimal algorithm for the given dataset from a repertoire of software-based turbulence correction algorithms. The work to be presented illustrates Fraunhofer IOSB's contribution to the first stage of the project. It consists of the selection of the most promising candidate methods for the demonstrator, as well as a detailed assessment of their limitations. A total of eight algorithms were selected. The first method performed global registration. For the correction of local motion, optical flow, block matching and iterative image generation methods were applied. To correct blur, we investigated various deconvolution approaches, including Richardson-Lucy approach and a blind deconvolution algorithm, and a Lucky fusion method. Finally, we focused on turbulence correction for scenes with moving content (people, vehicles). All methods were applied to several video sequences, which are recordings from field trials performed under different atmospheric conditions. The resulting corrected sequences were analysed first regarding their quantitative improvement in relation to the uncorrected sequences, and afterwards considering the execution time for each method. Three no-reference metrics were chosen for the quantitative analysis, namely Fisher information, edge variance and total variation. It was demonstrated that the selected methods achieved good correction results in many sequences. In particular optical flow and deconvolution achieved a high increase in image quality. Quantitative analysis was shown to be accurate, in the sense that it agreed systematically with visual perception. It was also demonstrated that certain methods produce good results within a short computation time, deconvolution for instance, which is of great interest for real-time use and it provides a good basis for future work

show abstract

Section: Methodsmentioning

confidence: 99%

The EDA TURBO project: software-based atmospheric turbulence mitigation

Hofmann

Goelzer²,

Gładysz³

2022

Environmental Effects on Light Propagation and Adaptive Systems V

View full text Add to dashboard Cite

show abstract

“…The results are shown in Figure 10 and Figure 11, but they are nearly identical to each other. A line spread function extraction technique [9] was chosen as a suitable method for assessing the relative quality of these images, but it indicated approximately no difference in quality regardless of whether the LWIR mask was provided. Image erosion and dilation algorithms were applied to the masks in order to tighten or loosen the constraint size, but there was still no improvement in the quality of the resultant images.…”

Section: Image Reconstruction With Lwir Maskingmentioning

confidence: 99%

Simultaneous processing of visible and long-wave infrared satellite imagery

Thompson

Werth

Calef

et al. 2015

2015 IEEE Aerospace Conference

Self Cite

View full text Add to dashboard Cite

One of the challenges of imaging satellites in the daytime is that the sun is generally behind the satellite from the observer's point of view. This means that much of the satellite structure can be in shadow at any given time. The Air Force Maui Optical and Supercomputing (AMOS) site's 3.6 meter telescope has the capability of recording data simultaneously in two bands of long-wave infrared (LWIR) as well as visible. This presents the possibility of performing joint processing of the infrared and visible imagery, which is appealing because the thermal imagery will not have any shadows. We describe exploitation strategies for this type of data, show the results of joint multiband processing, and compare with single-band images.

show abstract

“…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. For the ground-based Adaptive Optics images 9 , i) the pristine object image is totally unknown, so the full-reference quality metrics are unsuitable; ii) the image degradations made by atmospheric turbulence are different from the ones for natural imaging scenario, the effective metrics derived from natural image statistics tend to perform poor sensitivity for our images; iii) the structural information in object images are essential to the further identification and catalog of the space object 10 , which implies that we can employ the existing structure-feature based metrics to assess the image quality in the post-processing. Researchers 11 have proposed that the IQA models can be designed by using the earliest stage output of visual neural system, which in previous work have been modeled as a Laplacian of Gaussian (LOG) function.…”

Section: Introductionmentioning

confidence: 99%

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

Guo

Zhang²,

et al. 2015

SPIE Proceedings

View full text Add to dashboard Cite

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.

show abstract

A new performance metric for hybrid adaptive optics systems

Cited by 10 publications

References 10 publications

The EDA TURBO project: software-based atmospheric turbulence mitigation

The EDA TURBO project: software-based atmospheric turbulence mitigation

Simultaneous processing of visible and long-wave infrared satellite imagery

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

Contact Info

Product

Resources

About