Abstract
Abstract. This article describes a pipeline developed to automatically detect and correct motion blur due to the airplane motion in aerial images provided by a digital camera system with channel-dependent exposure times. Blurred images show anisotropy in their Fourier Transform coefficients that can be detected and estimated to recover the characteristics of the motion blur. To disambiguate the anisotropy produced by a motion blur from the possible spectral anisotropy produced by some periodic patterns present in a sharp image, we consider the phase difference of the Fourier Transform of two channel shot with different exposure times (i.e. with different blur extensions). This is possible because of the deep correlation between the three visible channels ensures phase coherence of the Fourier Transform coefficients in sharp images. In this context, considering the phase difference constitutes both a good detector and estimator of the motion blur parameters. In order to improve on this estimation, the phase difference is performed on local windows in the image where the channels are more correlated. The main lobe of the phase difference, where the phase difference between two channels is close to zero actually imitates an ellipse which axis ratio discriminates blur and which orientation and minor axis give respectively the orientation and the blur kernel extension of the long exposure-time channels. However, this approach is not robust to the presence in the phase difference of minor lobes due to phase sign inversions in the Fourier transform of the motion blur. They are removed by considering the polar representation of the phase difference. Based on the blur detection step, blur correction is eventually performed using two different approaches depending on the blur extension size: using either a simple frequency-based fusion for small blur or a semi blind iterative method for larger blur. The higher computing costs of the latter method make it only suitable for large motion blur, when the former method is not applicable.
Highlights
Since the late 1990’s, the development of airborne digital acquisition brought many improvements, especially in the radiometric quality of images where each pixel could be given a physical value after a radiometric calibration of the camera, which was not the case with silver film
In a previous work (Lelégard et al, 2010) we showed that considering the phase difference constitutes a robust way to detect images with motion blur
Even if the ringing artifacts are still there (Figure 7) the idea of looking for a more accurate model of kernel is justified by the fact that the ellipse detection (Figure 3) often overestimates the blur extension
Summary
Since the late 1990’s, the development of airborne digital acquisition brought many improvements, especially in the radiometric quality of images where each pixel could be given a physical value after a radiometric calibration of the camera, which was not the case with silver film. The only way to have leafless trees is to fly the mission between autumn and spring when the luminosity is weak. The exposure time should be increased, at the risk of causing motion blur. The images in which the blur is significant (more than 2 pixels) often represent a very small proportion of the mission. Our article proposes an automated pipeline that detects blurred images and removes the motion blur according to the blur extension. We will describe the channel-dependent exposure time camera for which our method is designed. We will review the previous work done on the topic of blur correction. Our pipeline will be presented in two parts: first, a blur detector taking advantage of the specificity of our camera, a step of correction that will depend on the blur extension. Some results on real images eventually illustrate the reliability and the relevance of the method
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