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

Artur Bal,Henryk Palus

doi:10.3390/s21217086

Abstract

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 (), 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.

Highlights

Comparing results from all tested models, it can be seen that, beyond the results for the interquartile range (IQR) measure obtained from C AM -B for s ∈ {2, 3}, the order of the obtained results is the same, i.e., the best results are given by the Smooth Local Polynomial (SLP) and Smooth Non-Iterative Local Polynomial (SNILP) models, slightly worse results are given by the polynomial 2D (P2D) model, and much worse results are obtained from the radial polynomial (RP) model
The comparison of results obtained from the SNILP model with these obtained from the P2D and RP models shows that the proposedmodel provides a better vignetting correction, in a sense of the used measures standard deviation (STD) e
Summarizing the results presented in this article, it can be stated that the SNILP

Summary

Introduction

The image vignetting is a phenomenon of the reduction of the brightness of an image from its optical center toward its edges. Considering the causes of vignetting, there are four main types of this phenomenon [1,2], listed it in the order of the place of its occurrence from an imaged scene to an image sensor, i.e., mechanical vignetting, optical vignetting, natural vignetting, and pixel vignetting. Mechanical vignetting refers to the occlusion of the light path by elements of lens-camera system, such as a filter mounted on a lens. This type of vignetting usually causes a 100%

Results

Discussion

Conclusion