Detecting and Correcting Motion Blur From Images Shot With Channel-Dependent Exposure Time

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.

Automatic detection of blurred images in UAV image sets

“…Furthermore, the use of AOIs to compensate for different terrains or hardware influences also has the advantage that no hardware modification are required (Raskar et al, 2006;Lelégard et al, 2012), which makes the proposed method easy to use. The application to real world images has shown that the method cannot only be applied to UAV datasets, which are suffering motion blur but also to close range datasets.…”

“…There are several different other methods that have been used for blur detection (Raskar et al, 2006;Lelégard et al, 2012). A useful method developed by Crete et al (2007) is based on the human perception of blur.…”

Uav Image Blur – Its Influence and Ways to Correct It

“…Then the images are transferred to the frequency domain using a Fourier transformation. It is well established that high frequencies are absent in blurred images (Lelégard et al, 2012). The absence of high frequencies can be compensated by enhancing the blurred image using high frequencies extracted from the sharp image.…”

“…The findings also support work of Shah and Schickler (2012) who developed blur correction methods specifically for UAV applications. Lelégard et al 2012 states that blur larger than 2 pixels is significant. Our findings would contradict this, suggesting that a blur of just 2 pixels is actually too small to influence detection, identification, referencing and measurement.…”

“…Non-blind image deconvolution methods can be carried out in various ways and requires additional knowledge. Additional information can be gained through a variety of methods including, other overlapping images (Agrawal et al, 2012), precise IMU measurements (Joshi et al, 2008), video cameras (Tai et al, 2010), fluttering shutters (Raskar et al, 2006) or colour channel dependent exposure times (Lelégard et al, 2012). The main aim of these methods is to establish an appropriate blur kernel, which can be used by established deblurring algorithms.…”