Subpixel rendering

About: Subpixel rendering is a research topic. Over the lifetime, 3885 publications have been published within this topic receiving 82789 citations.

On the possibility of automatic multisensor image registration

Abstract: Multisensor image registration is needed in a large number of applications of remote sensing imagery. The accuracy achieved with usual methods (manual control points extraction, estimation of an analytical deformation model) is not satisfactory for many applications where a subpixel accuracy for each pixel of the image is needed (change detection or image fusion, for instance). Unfortunately, there are few works in the literature about the fine registration of multisensor images and even less about the extension of approaches similar to those based on fine correlation for the case of monomodal imagery. In this paper, we analyze the problem of the automatic multisensor image registration and we introduce similarity measures which can replace the correlation coefficient in a deformation map estimation scheme. We show an example where the deformation map between a radar image and an optical one is fully automatically estimated.

Electronic speckle photography: analysis of an algorithm giving the displacement with subpixel accuracy.

Abstract: Replacing photographic recording by electronic processing has some obvious advantages. An algorithm used for electronic speckle pattern photography is presented, and the reliability and accuracy is analyzed by using computer-generated speckle patterns. The algorithm is based on a two-dimensional discrete cross correlation between subimages from different images. Subpixel accuracy is obtained by a Fourier series expansion of the discrete correlation surface. The accuracy of the algorithm was found to vary in proportion to sigma/n(1 - delta)(2), where sigma is the speckle size, n is the subimage size, and delta is the amount of decorrelation, with negligible systematic errors. For typical values the uncertainty in the displacement is approximately 0.05 pixels. The uncertainty is found to increase with increased displacement gradients.

A Region-Based Approach to Digital Image Registration with Subpixel Accuracy

Abstract: Automatic registration of images with translational, rotational, and scaling differences is discussed. To register two images from the same scene, first, the images are segmented and closedboundary regions in the images are extracted. Next, centers of gravity of closed-boundary regions are taken as control points and correspondence is established between the control points. Using this correspondence, the original images are then revisited and the segmentation process is refined in such a way that the obtained corresponding regions become optimally similar. This enables determination of centers of gravity of the regions up to subpixel accuracy. Finally, registration parameters are determined by the least squares error criterion.

Least squares subspace projection approach to mixed pixel classification for hyperspectral images

Abstract: An orthogonal subspace projection (OSP) method using linear mixture modeling was recently explored in hyperspectral image classification and has shown promise in signature detection, discrimination, and classification. In this paper, the OSP is revisited and extended by three unconstrained least squares subspace projection approaches, called signature space OSP, target signature space OSP, and oblique subspace projection, where the abundances of spectral signatures are not known a priori but need to be estimated, a situation to which the OSP cannot be directly applied. The proposed three subspace projection methods can be used not only to estimate signature abundance, but also to classify a target signature at subpixel scale so as to achieve subpixel detection. As a result, they can be viewed as a posteriori OSP as opposed to OSP, which can be thought of as a priori OSP. In order to evaluate these three approaches, their associated least squares estimation errors are cast as a signal detection problem ill the framework of the Neyman-Pearson detection theory so that the effectiveness of their generated classifiers can be measured by receiver operating characteristics (ROC) analysis. All results are demonstrated by computer simulations and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data.

NoRMCorre: An online algorithm for piecewise rigid motion correction of calcium imaging data

Abstract: Motion correction is a challenging pre-processing problem that arises early in the analysis pipeline of calcium imaging data sequences. Here we introduce an algorithm for fast Non-Rigid Motion Correction (NoRMCorre) based on template matching. orm operates by splitting the field of view into overlapping spatial patches that are registered for rigid translation against a continuously updated template. The estimated alignments are subsequently up-sampled to create a smooth motion field for each frame that can efficiently approximate non-rigid motion in a piecewise-rigid manner. orm allows for subpixel registration and can be run in an online mode resulting in comparable to or even faster than real time motion registration on streaming data. We evaluate the performance of the proposed method with simple yet intuitive metrics and compare against other non-rigid registration methods on two-photon calcium imaging datasets. Open source Matlab and Python code is also made available.