Fast vignetting reduction method for digital still camera

Abstract

The effect of vignetting is undesirable in image processing and analysis. It cause fall-off of pixel intensity from centre towards edges of the image. In this paper, we propose a new procedure of fast vignetting reduction based on two images acquired with different camera/lens settings. The change of lens aperture or focal length will also change the effect of vignetting in images. These differences are used to estimate a vignetting function, which is used for vignetting reduction. The obtained image after vignetting reduction is not significantly different from the image without vignetting.

Vignetting Compensation Method for CMOS Camera Based on LED Spatial Array

“…Nevertheless, in practical applications, an inherent characteristic of camera imaging known as vignetting negatively impacts the consistency calibration process. Vignetting refers to the progressive decrease in image brightness as the distance from the camera's optical axis increases [1], a phenomenon that is particularly pronounced during LED screen brightness calibration. When employing matrix cameras to gather data for uniformity correction, the natural attenuation of brightness from the central to the peripheral areas can lead to hardware correction coefficients that underestimate the center region and overestimate the edge regions [2].…”

Digital forensic analysis for source video identification: A survey

“…Figure 1 shows the common techniques that have been used in source camera identification based on digital images, which can be divided into several classes: digital camera identification based on PRNU estimation Lukas et al (2006); Mieremet (2019); Lawgaly and Khelifi (2016), statistical methods Chapman et al (2015), dark signal identification Virmontois et al (2010), sensor dust Dirik et al (2008), optical defects Kordecki et al (2015), and machine learning, such as deep models Yang et al (2019) involving convolutional neural networks. However, source camera identification methods on videos have focused more on PRNU and machine learning methods.…”