A study of vignetting correction methods in camera colorimetric calibration

Kordecki, Andrzej; Bal, A.; Palus, Henryk

doi:10.1117/12.2268532

Cited by 4 publications

(11 citation statements)

References 7 publications

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“…4.7, with correction factors up to 2.79. Similarly large correction factors have been found for other cameras, for instance approximately 2.8 in a Canon EOS 400D [19] and 650D [20], 4 in a Raspberry Pi camera [57], 1.8 in a Canon EOS 10D [71], and 1.5 in a Nikon E775 [72]. It should be noted that vignetting is highly aperture-dependent and thus these correction factors will change with varying apertures [71].…”

Section: Discussionsupporting

confidence: 59%

“…As discussed in Sect. 2, many further alternative flat-fielding methods exist [19,20,[70][71][72][73][74]. Our data may be useful as a ground truth for a thorough comparison of such methods akin to [20,54].…”

Section: Discussionmentioning

confidence: 99%

“…Finally, flat-fielding (specifically vignetting) is typically corrected using a pre-made correction map. A variety of methods for generating such maps exists, based for example on computational methods using regular photographs [20,[70][71][72][73], simply averaging many exposures [19], and simply imaging white paper [74]. These maps are typically parametrized, for which various methods also exist [19,20,57,63,[71][72][73][74], the simplest being the cos 4 model, a combination of inverse square falloff, Lambert's law, and foreshortening [71].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, these results differed wildly between camera models, highlighting the importance of standardization and a centralized database. accuracy of color measurements and their conversion to standard measures, such as the CIE 1931 XYZ and CIELAB color spaces, is limited by distortions in the observed colors [20] and differences in spectral response functions [21,[23][24][25].Extensive (spectro-)radiometric calibrations of consumer cameras are laborious and require specialized equipment and are thus not commonly performed [23,54,60]. A notable exception is the spectral and absolute radiometric calibration of a Raspberry Pi 3 V2 webcam by Pagnutti et al [57], including calibrations of linearity, exposure stability, thermal and electronic noise, flat-field, and spectral response.…”

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confidence: 99%

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Standardized spectral and radiometric calibration of consumer cameras

Burggraaff¹,

Schmidt²,

Zamorano³

et al. 2019

Opt. Express

108

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Consumer cameras, particularly onboard smartphones and UAVs, are now commonly used as scientific instruments. However, their data processing pipelines are not optimized for quantitative radiometry and their calibration is more complex than that of scientific cameras. The lack of a standardized calibration methodology limits the interoperability between devices and, in the ever-changing market, ultimately the lifespan of projects using them. We present a standardized methodology and database (SPECTACLE) for spectral and radiometric calibrations of consumer cameras, including linearity, bias variations, read-out noise, dark current, ISO speed and gain, flat-field, and RGB spectral response. This includes golden standard ground-truth methods and do-it-yourself methods suitable for non-experts. Applying this methodology to seven popular cameras, we found high linearity in RAW but not JPEG data, inter-pixel gain variations >400% correlated with large-scale bias and read-out noise patterns, non-trivial ISO speed normalization functions, flat-field correction factors varying by up to 2.79 over the field of view, and both similarities and differences in spectral response. Moreover, these results differed wildly between camera models, highlighting the importance of standardization and a centralized database. accuracy of color measurements and their conversion to standard measures, such as the CIE 1931 XYZ and CIELAB color spaces, is limited by distortions in the observed colors [20] and differences in spectral response functions [21,[23][24][25].Extensive (spectro-)radiometric calibrations of consumer cameras are laborious and require specialized equipment and are thus not commonly performed [23,54,60]. A notable exception is the spectral and absolute radiometric calibration of a Raspberry Pi 3 V2 webcam by Pagnutti et al. [57], including calibrations of linearity, exposure stability, thermal and electronic noise, flat-field, and spectral response. Using this absolute radiometric calibration, digital values can be converted into SI units of radiance. However, the authors noted the need to characterize a large number of these cameras before the results could be applied in general. Moreover, certain calibrations are device-dependent and would need to be done separately on each device. Spectral and radiometric calibrations of seven cameras, including the Raspberry Pi, are given in [51]. These calibrations include dark current, flat-fielding, linearity, and spectral characterization. However, for the five digicams included in this work, JPEG data were used, severely limiting the quality and usefulness of these calibrations, as described above.Spectral characterizations are more commonly published since these are vital for quantitative color analysis. Using various methods, the spectral responses of several Canon [1,19,23,24,46,51,54,61], Nikon [1,13,23,24,54,[59][60][61][62], Olympus [23, 24, 51], Panasonic [46], SeaLife [13], Sigma [60], and Sony [1, 23, 46, 51, 61] digital cameras (digicams), as well as a number of smartpho...

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Section: Discussionsupporting

confidence: 59%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Standardized spectral and radiometric calibration of consumer cameras

Burggraaff¹,

Schmidt²,

Zamorano³

et al. 2019

Opt. Express

108

View full text Add to dashboard Cite

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Digital camera identification based on analysis of optical defects

Bernacki

2019

Multimed Tools Appl

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In this paper we deal with the problem of digital camera identification by photographs. Identifying camera is possible by analyzing camera's sensor artifacts that occur during the process of photo processing. The problem of digital camera identification has been popular for a long time. Recently many effective and robust algorithms for solving this problem have been proposed. However, almost all solutions are based on state-of-the-art algorithm, proposed by Lukás et al. in 2006. Core of this algorithm is to calculate the so-called sensor pattern noise based on denoising images with wavelet-based denoising filter. Such technique is very efficient, but very time consuming. In this paper we consider tracing cameras by analyzing defects of their optical systems, like vignetting and lens distortion. We show that analysis of vignetting defect allows for recognizing brand of the camera. Lens distortion can be used to distinguish images from different cameras. Experimental evaluation was carried out on 60 devices (compact cameras and smartphones) for a total number of 12 051 images, with support of the Dresden Image Database. Proposed methods do not require denoising images with wavelet-based denoising filter what has a significant influence for speed of image processing, compared with state-of-the-art algorithm.

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Vignetting Correction Based on a Two-Dimensional Gaussian Filter With Harmony for Area Array Sensors

Cao

Zhang³

et al. 2022

IEEE Trans. Comput. Imaging

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Vignetting is the main factor causing uneven brightness in an image due to the inherent characteristics of the camera sensor. In radiometric correction-based remote sensing sensors, vignetting can lead to uncomparable radiation signals within and between images, and in the processing of remote sensing images, it leads to an unbalanced color when images are mosaiced. Due to distortions of optical devices, uneven response of detectors and other factors, it is difficult to extract the vignetting from complex reference images and correct it accurately. In this study, a low-pass Gaussion filter was selected to extract the vignetting from reference image, and the standard deviation (STD) and mean of the reference images were used as the factor of harmony to evaluate the strength of the filter for attaining optimal vignetting background. In experiments, the method proposed was compared with polynomial fitting method and general Gaussian filter mothed. The results show that Gaussian filter with harmony method achieves the best effect of vignetting correction compared with the other two methods.

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A study of vignetting correction methods in camera colorimetric calibration

Cited by 4 publications

References 7 publications

Standardized spectral and radiometric calibration of consumer cameras

Standardized spectral and radiometric calibration of consumer cameras

Digital camera identification based on analysis of optical defects

Vignetting Correction Based on a Two-Dimensional Gaussian Filter With Harmony for Area Array Sensors

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