Local polynomial model: a new approach to vignetting correction

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

doi:10.1117/12.2268566

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…In this section, we present an analysis comparing results of images corrected using three methods: (1) vignetting image correction based on polynomial fitting [ 35 ]; (2) laboratory coefficients; (3) on-orbit coefficients based on a lookup table from histogram matching images between February and April 2015; (4) the proposed side-slither-based method.…”

Section: Resultsmentioning

confidence: 99%

Side-Slither Data-Based Vignetting Correction of High-Resolution Spaceborne Camera with Optical Focal Plane Assembly

Chen

Pan

Wang

et al. 2018

Sensors

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Optical focal plane assemblies are increasingly being used in high-resolution optical satellite systems to enhance the width of the image using linear push-broom imaging. With this system, vignetting occurs in the area of overlap, affecting image quality. In this paper, using the characteristics of the side-slither data, we propose side-slither data-based vignetting correction of a high-resolution spaceborne camera with an optical focal plane assembly. First, the raw side-slither data standardization is used to ensure that each row has the same features. Then, with the spatial correlation of a gray-level co-occurrence matrix, the gray-level co-occurrence matrix is proposed to identify the uniform regions, to extract the sample points. Finally, due to the characteristics of compatible linear response and non-linear response, the power-law model was used to fit, and the Levenberg–Marquardt algorithm was used to fit the model. In the experiment, polynomial fitting, laboratory coefficients and on-orbit coefficients were used for comparison with the proposed method. The side-slither data can be treated as a uniform scene due to their characteristics, and the side-slither image that was corrected using the proposed method showed less than 1% change in mean value, a root-mean-square deviation value better than 0.1%, and the average streaking metrics were superior to 0.02. The results showed that the proposed method performs significantly better in the vignetting area.

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Section: Resultsmentioning

confidence: 99%

Side-Slither Data-Based Vignetting Correction of High-Resolution Spaceborne Camera with Optical Focal Plane Assembly

Chen

Pan

Wang

et al. 2018

Sensors

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“…A possible solution to this problem uses the idea of decomposition of the 2D approximation problem into many interrelated 1D approximation problems. This approach was presented by Reference [27][28][29]. The results obtained so far in this area are briefly described and summarized in Section 2.…”

Section: Objective Of the Workmentioning

confidence: 99%

“…The more general Local Polynomial (LP) model of vignetting was proposed in Reference [28]. In this model, the degree of polynomial s used as 1D regression function is not limited to degree of 2 but can be freely chosen; hence, V x j and V y i are calculated as follows:…”

Section: Local Polynomial (Lp) Modelmentioning

confidence: 99%

A Smooth Non-Iterative Local Polynomial (SNILP) Model of Image Vignetting

Bal

Palus

2021

Sensors

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Image vignetting is one of the major radiometric errors, which occurs in lens-camera systems. In many applications, vignetting is an undesirable phenomenon; therefore, when it is impossible to fully prevent its occurrence, it is necessary to use computational methods for its correction in the acquired image. In the most frequently used approach to the vignetting correction, i.e., the flat-field correction, the usage of appropriate vignetting models plays a crucial role. In the article, the new model of vignetting, i.e., Smooth Non-Iterative Local Polynomial (SNILP) model, is proposed. The SNILP model was compared with the models known from the literature, e.g., the polynomial 2D and radial polynomial models, in a series of numerical tests and in the real-data experiment. The obtained results prove that the SNILP model usually gives better vignetting correction results than the other aforementioned tested models. For images larger than UXGA format (1600×1200), the proposed model is also faster than other tested models. Moreover, among the tested models, the SNILP model requires the least hardware resources for its application. This means that the SNILP model is suitable for its usage in devices with limited computing power.

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“…It is also pointed that vignetting can be described by natural image statistics. In [23][24][25] polynomial models for vignetting correction are proposed.…”

Section: Related and Previous Workmentioning

confidence: 99%

“…Types of vignetting are precisely described in [10]. This topic has attracted many researchers, therefore a lot of algorithms [6,[22][23][24] and patents [27] for vignetting correction are developed. Lens distortion is a deviation from rectilinear projection [5,32].…”

Section: Introductionmentioning

confidence: 99%

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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Local polynomial model: a new approach to vignetting correction

Cited by 4 publications

References 13 publications

Side-Slither Data-Based Vignetting Correction of High-Resolution Spaceborne Camera with Optical Focal Plane Assembly

Side-Slither Data-Based Vignetting Correction of High-Resolution Spaceborne Camera with Optical Focal Plane Assembly

A Smooth Non-Iterative Local Polynomial (SNILP) Model of Image Vignetting

Digital camera identification based on analysis of optical defects

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