AAM: AN Assessment Metric of Axial Chromatic Aberration

Helou, Majed El; Dümbgen, Frederike; Siisstrunk, Sabine

doi:10.1109/icip.2018.8451377

Cited by 11 publications

(6 citation statements)

References 18 publications

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“…In practice, f Y and f Z are close between RGB channels, making the depths d 0 Y and d 0 Z close, hence decreasing α towards values close to 0. Note also that more complex lenses are designed to correct color chromatic aberration and minimize the shift between color focal planes [31]. We show in Section 4.1 that the shift is nevertheless detectable even with complex lenses, and that when using NIR the corresponding α value becomes noticeably larger, allowing for a more robust solution (Fig.…”

Section: Mathematical Framework and Solutionmentioning

confidence: 74%

Solving the depth ambiguity in single-perspective images

Helou¹,

Shahpaski²,

Süsstrunk³

2019

OSA Continuum

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Scene depth estimation is gaining in importance as more and more AR/VR and robot vision applications are developed. Conventional depth-from-defocus techniques can passively provide depth maps from a single image. This is especially advantageous for moving scenes. However, they suffer a depth ambiguity problem where two distinct depth planes can have the same amount of defocus blur in the captured image. We solve the ambiguity problem and, as a consequence, introduce a passive technique that provides a one-to-one mapping between depth and defocus blur. Our method relies on the fact that the relationship between defocus blur and depth is also wavelength dependent. The depth ambiguity is thus solved by leveraging (multi-) spectral information. Specifically, we analyze the difference in defocus blur of two channels to obtain different scene depth regions. This paper provides the derivation of our solution, a robustness analysis, and validation on consumer lenses.

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Section: Mathematical Framework and Solutionmentioning

confidence: 74%

Solving the depth ambiguity in single-perspective images

Helou¹,

Shahpaski²,

Süsstrunk³

2019

OSA Continuum

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“…However, since each channel has a different wavelength, they affect D I differently. This can be shown by combining the Lens Maker equation, [43], [44], and Cauchy equation, [45], , given by:…”

Section: Motivationmentioning

confidence: 99%

Focus Measurement in Color Space for Shape From Focus Systems

et al. 2021

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Shape from Focus (SFF) has been studied extensively in computer vision for 3D shape and depth recovery. The first stage in SFF methods is to compute the focus value of every pixel by converting the colored images into gray scale and then apply the focus measure operator. Converting colored values in the images into gray scale values may lead to imprecise mapping of pixels with different colored values onto the same gray scale value, this affects the overall accuracy of the system. In a colored image, the focused pixels maintain a considerable color difference from their neighboring pixels as compared to the defocused ones, which are blended into their neighborhood. This article presents an alternative method to measure the degree of focus by directly processing colored images. The color differences of the neighbor pixels with respect to the central pixel are obtained and summed together, this is followed by calculating their spread. The sum and the spread are combined to measure the degree of focus of the pixel in consideration. The proposed focus measure is then used for shape recovery of various simulated and real objects and is compared with previous techniques. The comparison results show the proposed method has the highest correlation and smallest RMSE values confirming the effectiveness of using color images for shape recovery.

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“…Many SR methods thus model the anti-aliasing filter as a 2D Gaussian kernel, attempting to mimic the point spread function (PSF) of capturing devices [15,43,54]. In practice, even a single imaging device results in multiple kernels, depending on its settings [17]. For real images, the kernel can also be different from a Gaussian kernel [16,22].…”

Section: Super-resolutionmentioning

confidence: 99%

Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks

Helou¹,

Zhou²,

Süsstrunk³

2020

Preprint

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Super-resolution and denoising are ill-posed yet fundamental image restoration tasks. In blind settings, the degradation kernel or the noise level are unknown. This makes restoration even more challenging, notably for learningbased methods, as they tend to overfit to the degradation seen during training. We present an analysis, in the frequency domain, of degradation-kernel overfitting in super-resolution and introduce a conditional learning perspective that extends to both super-resolution and denoising. Building on our formulation, we propose a stochastic frequency masking of images used in training to regularize the networks and address the overfitting problem. Our technique improves stateof-the-art methods on blind super-resolution with different synthetic kernels, real super-resolution, blind Gaussian denoising, and real-image denoising.

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AAM: AN Assessment Metric of Axial Chromatic Aberration

Cited by 11 publications

References 18 publications

Solving the depth ambiguity in single-perspective images

Solving the depth ambiguity in single-perspective images

Focus Measurement in Color Space for Shape From Focus Systems

Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks

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