Genetic algorithm is applied to exploit the correlation of image pixel values to obtain better estimation of neighboring pixel values, which results in optimal balance between information storage capacity and imperceptibility.
By minimizing the reconstruction error, the generalized low-rank approximation of matrices (GLRAM) [23] took into account the spatial correlation of image pixels within a localized neighborhood and applied bilinear transforms to the input image matrices.
For filtering multichannel images, two approaches are possible, namely, component-wise denoising and vector or 3D processing taking into account inter-channel correlation of image data [1, 2, 6, 12, 33, 34].
Moreover, the correlation of image variations at neighboring pixels is explicitly utilized to achieve robust detection performance since neighboring pixels tend to be similarly affected by environmental effects (e.g., dynamic scenes).
As in our work, the algorithm in [22] also processes image intensities directly, but, unlike our problem setup, the observation model in [22] does not incorporate any information about spatial correlation of image pixels, treating instead each pixel as independent observations.
Correlation-based techniques are generally advantageous over single particle tracking schemes since they rely on the correlation of image subsets with 40 80 particles on average and hence are much less sensitive to spatial and temporal intensity variation of individual particles.
Anderson et al. (2009) propose a registration pipeline for large ssTEM datasets based on correlation of image intensities within and across sections.
Comparing these numbers to image quality scores seen in the above study shows a direct correlation of image quality to detection rate for ERM, but there is no correlation of detection rate to axial resolution.
In this study, we therefore aimed at describing the correlation of image quality of coronary artery stents and heart rate using 64-slice MDCT in an ex-vivo setting by using an anthropomorphic moving heart phantom.
