Fast vignetting correction and color matching for panoramic image stitching

Doutre, Colin; Nasiopoulos, Panos

doi:10.1109/icip.2009.5414326

Cited by 22 publications

(14 citation statements)

References 8 publications

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“…The gains are computed using an error function, which is the sum of gain normalized intensity errors for all overlapping pixels. Doutre and Nasiopoulos [24] use a second-order polynomial to correct each pixel in the target image using the exposure and white balance of I ref . The polynomial weights are computed by comparing the overlapping regions of images using standard linear least-squares regression.…”

Section: Literature Surveymentioning

confidence: 99%

Automatic selection of color reference image for panoramic stitching

et al. 2015

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Section: Literature Surveymentioning

confidence: 99%

Automatic selection of color reference image for panoramic stitching

et al. 2015

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“…This pixel‐wise division, accomplished according to Eq. (2) (Souchier et al , 2004), is known as flat field (Jericevic et al , 1989; Model, 2001; Model & Burkhardt, 2001; Model et al , 2009) or retrospective correction (Young, 2001; Babaloukas et al , 2011): B is the noise relative to the acquisition system and is often neglected (Yu, 2004; Zwier et al , 2004; Doutre & Nasiopoulos, 2009) as in the present work. C is a multiplicative constant to bring the values of the flat field corrected image I FFC ( x ) in a given range.…”

Section: Modelmentioning

confidence: 99%

“…B is the noise relative to the acquisition system and is often neglected (Yu, 2004;Zwier et al, 2004;Doutre & Nasiopoulos, 2009) as in the present work. C is a multiplicative constant to bring the values of the flat field corrected image I FFC (x) in a given range.…”

Section: Modelmentioning

confidence: 99%

Multi‐image based method to correct vignetting effect in light microscopy images

Piccinini

Lucarelli

Gherardi

et al. 2012

Journal of Microscopy

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SummaryVignetting is the radial attenuation effect of the image's brightness intensity from the center of the optical axis to the edges. To perform quantitative image analyses it is mandatory to take into account this effect, intrinsic of the acquisition system. Many image processing steps, such as segmentation and object tracking, are strongly affected by vignetting and the effect becomes particularly evident in mosaicing. The most common approach to compensate the attenuation of the image's brightness intensity is to estimate the vignetting function from a homogeneous reference object, typically an empty field, and to use it to normalize the images acquired under the same microscope set-up conditions. However, several reasons lead to the use of image-based methods to estimate the vignetting function from the images themselves. In this work, we propose an effective multiimage based method suitable for real-time applications. It is designed to correct vignetting in wide field light microscopy images. The vignetting function is computed stemming from a background built incrementally from the proposed background segmentation algorithm, validated on several manually segmented images. The extensive experiments carried out using cell cultures, histological samples and synthetic images prove that our method almost always yields the best results and in worst cases are comparable to those achieved by using homogeneous reference objects.

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“…The effect of vignetting on the image is undesirable in image processing and analysis, particularly in areas such as: image denoising [6], image segmentation [23], microscopic image analysis [17,18], sky image analysis [20], visual surveillance [10], motion analysis in video sequences [1] and panoramic images [9,16]. Therefore, from the viewpoint of image processing, it is important to reduce vignetting in image.…”

Section: Introductionmentioning

confidence: 99%

Practical vignetting correction method for digital camera with measurement of surface luminance distribution

Kordecki

Palus

Bal

2016

SIViP

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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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Fast vignetting correction and color matching for panoramic image stitching

Cited by 22 publications

References 8 publications

Automatic selection of color reference image for panoramic stitching

Automatic selection of color reference image for panoramic stitching

Multi‐image based method to correct vignetting effect in light microscopy images

Practical vignetting correction method for digital camera with measurement of surface luminance distribution

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