Impact of detector-element active-area shape and fill factor on super-resolution

Hardie, Russell C.; Droege, Douglas R.; Dapore, Alexander J.; Greiner, Mark E.

doi:10.3389/fphy.2015.00031

Cited by 8 publications

(6 citation statements)

References 18 publications

(51 reference statements)

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“…However, when upsampling an image for SR, the native detector active area will now span multiple HR pixels and play an important and potentially dominant role in the observation model. The detector OTF can be found by simply taking the 2D Fourier transform of the detector element shape as described by Hardie et al 34 In our study, we wish to present a fair comparison between rectangular and hexagonal sampling. To do so, we have elected to make the active area of both types of detectors the same and have 100% fill factor.…”

Section: Optical Transfer Functionmentioning

confidence: 99%

“…Equivalently, this means we must have p < λF/2 to guarantee no aliasing. 34 An impulse-invariant equivalent discrete-space PSF, h d (n 1 , n 2 ), may be found by sampling h(x, y) with a sample spacing of p. This impulse-invariant PSF is applied to produce…”

Section: Optical Transfer Functionmentioning

confidence: 99%

“…Our observation model effectively constitutes a "forward model" for the inverse problem we wish to solve. The observation model leverages a camera-specific optical transfer function (OTF) that models diffraction and detector integration (based on either hexagonal or rectangular detectors) for generating our data 33,34 as well as a resampling step. We apply the observation model to the DIV2K SISR dataset 35 to generate data to train our models so as to be ready to process real full frame RGB color camera outputs.…”

Section: Introductionmentioning

confidence: 99%

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Resampling and super-resolution of hexagonally sampled images using deep learning

Flaute,

Hardie,

Elwarfalli

2021

Preprint

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Super-resolution (SR) aims to increase the resolution of imagery. Applications include security, medical imaging, and object recognition. We propose a deep learning-based SR system that takes a hexagonally sampled lowresolution image as an input and generates a rectangularly sampled SR image as an output. For training and testing, we use a realistic observation model that includes optical degradation from diffraction and sensor degradation from detector integration. Our SR approach first uses non-uniform interpolation to partially upsample the observed hexagonal imagery and convert it to a rectangular grid. We then leverage a state-of-the-art convolutional neural network (CNN) architecture designed for SR known as Residual Channel Attention Network (RCAN). In particular, we use RCAN to further upsample and restore the imagery to produce the final SR image estimate. We demonstrate that this system is superior to applying RCAN directly to rectangularly sampled LR imagery with equivalent sample density. The theoretical advantages of hexagonal sampling are well known. However, to the best of our knowledge, the practical benefit of hexagonal sampling in light of modern processing techniques such as RCAN SR is heretofore untested. Our SR system demonstrates a notable advantage of hexagonally sampled imagery when employing a modified RCAN for hexagonal SR.

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Section: Optical Transfer Functionmentioning

confidence: 99%

Section: Optical Transfer Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Resampling and super-resolution of hexagonally sampled images using deep learning

Flaute,

Hardie,

Elwarfalli

2021

Preprint

Self Cite

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“…The detector OTF is the Fourier transform of the detector active area shape. 1 Finally, the atmospheric turbulence model is based on that originally derived by Fried…”

Section: Interpolation Impulse Responsementioning

confidence: 99%

“…However, practical limitations associated with a small detector size often lead to employing focal plane arrays that do not meet the Nyquist criterion under diffraction-limited imaging conditions. 1 Such undersampling may prevent the imaging system from achieving the full resolution afforded by the optics. A wide variety of superresolution (SR) algorithms have been proposed to successfully address such undersampling using multiple unique frames with relative motion between the scene and camera.…”

Section: Introductionmentioning

confidence: 99%

Fusion of interpolated frames superresolution in the presence of atmospheric optical turbulence

et al. 2019

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An extension of the fusion of interpolated frames superresolution (FIF SR) method to perform SR in the presence of atmospheric optical turbulence is presented. The goal of such processing is to improve the performance of imaging systems impacted by turbulence. We provide an optical transfer function analysis that illustrates regimes where significant degradation from both aliasing and turbulence may be present in imaging systems. This analysis demonstrates the potential need for simultaneous SR and turbulence mitigation (TM). While the FIF SR method was not originally proposed to address this joint restoration problem, we believe it is well suited for this task. We propose a variation of the FIF SR method that has a fusion parameter that allows it to transition from traditional diffraction-limited SR to pure TM with no SR as well as a continuum in between. This fusion parameter balances subpixel resolution, needed for SR, with the amount of temporal averaging, needed for TM and noise reduction. In addition, we develop a model of the interpolation blurring that results from the fusion process, as a function of this tuning parameter. The blurring model is then incorporated into the overall degradation model that is addressed in the restoration step of the FIF SR method. This innovation benefits the FIF SR method in all applications. We present a number of experimental results to demonstrate the efficacy of the FIF SR method in different levels of turbulence. Simulated imagery with known ground truth is used for a detailed quantitative analysis. Three real infrared image sequences are also used. Two of these include bar targets that allow for a quantitative resolution enhancement assessment.

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Impact of super-resolution and image acquisition on the detection of calcifications in digital breast tomosynthesis

Barufaldi,

Acciavatti,

Conant

et al. 2023

Eur Radiol

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Impact of detector-element active-area shape and fill factor on super-resolution

Cited by 8 publications

References 18 publications

Resampling and super-resolution of hexagonally sampled images using deep learning

Resampling and super-resolution of hexagonally sampled images using deep learning

Fusion of interpolated frames superresolution in the presence of atmospheric optical turbulence

Impact of super-resolution and image acquisition on the detection of calcifications in digital breast tomosynthesis

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