A smooth local polynomial model of vignetting

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

doi:10.1109/mmar.2017.8046944

Cited by 5 publications

(2 citation statements)

References 16 publications

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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%

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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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Section: Objective Of the Workmentioning

confidence: 99%

“…As a solution to this problem, in Reference [29], the Smooth Local Polynomial (SLP) model was proposed. The core idea of this method is the iterative repetition of the LP model estimation, i.e.,…”

Section: Smooth Local Polynomial (Slp) Modelmentioning

confidence: 99%

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

Bal

Palus

2021

Sensors

Self Cite

View full text Add to dashboard Cite

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Defect Enhancement of Image Sensor Based on Multichannel Decomposition and Log-Intensity Entropy

Yue,

Li,

Wang

et al. 2024

IEEE Trans. Consumer Electron.

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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 smooth local polynomial model of vignetting

Cited by 5 publications

References 16 publications

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

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

Defect Enhancement of Image Sensor Based on Multichannel Decomposition and Log-Intensity Entropy

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

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