Vignetting refers to the fall-off pixel intensity from the centre towards the edges of the image. This effect is undesirable in image processing and analysis. In the literature, the most commonly used methods of vignetting correction assume radial characteristic of vignetting. In the case of camera lens systems with non-radial vignetting, such approach leads to insufficient correction. Additionally, the majority of vignetting correction methods need a reference image acquired from a uniformly illuminated scene, what can be difficult to achieve. In this paper, we propose a new method of vignetting correction based on the local parabolic model of non-radial vignetting and compensation of non-uniformity of scene luminance. The new method was tested on camera lens system with non-radial vignetting and non-uniformly illuminated scene. In these conditions, the proposed method gave the best correction results among the tested methods.

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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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