Image Interpolation

2D MRSI suffers from the effect of the spatial response function due to the truncation of the sampling of k-space. Filtering of the k-space data-set is often used to suppress the side lobes caused by the effects of the SRF, where the sampled data-set is multiplied with a weighting function before inverse FT. Commonly used filters in MRSI are the cosine, Hanning and Hamming filters. The data-set is often interpolated into a larger image matrix size for analysis, where "Fourier interpolation" (FoI) and "cubic spline interpolation" (CSpI) are two common methods. In this work, the effects of k-space filtering in practical usage was examined, and the image representations of the object for the two interpolation methods were compared.

Channel-to-channel co-registration is an important performance metric for the Geostationary Operational Environmental Satellite (GOES) Imager, and large co-registration errors can have a significant impact on the reliability of derived products that rely on combinations of multiple infrared (IR) channels. Affected products include the cloud mask, fog and fire detection. This is especially the case for GOES-13, in which the co-registration error between channels 2 (3.9 μm) and 4 (10.7 μm) can be as large as 1 pixel (or ∼4 km) in the east-west direction. The GOES Imager IR channel-to-channel co-registration characterization (GII4C) algorithm is presented, which allows a systematic calculation of the co-registration error between GOES IR channel image pairs. The procedure for determining the co-registration error as a function of time is presented. The algorithm characterizes the co-registration error between corresponding images from two channels by spatially transforming one image using the fast Fourier transformation resampling algorithm and determining the distance of the transformation that yields the maximum correlation in brightness temperature. The GII4C algorithm is an area-based approach which does not depend on a fixed set of control points that may be impacted by the presence of clouds. In fact, clouds are a feature that enhances the correlations. The results presented show very large correlations over the majority of Earth-viewing pixels, with stable algorithm results. Verification of the algorithm output is discussed, and a global spatial-spectral gradient asymmetry parameter is defined. The results show that the spatial-spectral gradient asymmetry is strongly correlated to the co-registration error and can be an effective global metric for the quality of the channel-to-channel co-registration characterization algorithm. Implementation of the algorithm in the GOES ground system is presented. This includes an offline component to determine the time dependence of the co-registration errors and a real-time component to correct the co-registration errors based on the inputs from the offline component. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

This paper presents a swvey on existing as wet1 as new trends in the field of high resolution (HR) image processing. The paper focuses on main topics, image registration, image interpolation and image superresolution reconstruction. The problem is defined, current techniques are presented. For example, in image interpolation, polynomial hased image interpolation is reviewed, some new techniques for adaptive image interpolation and inverse image interpolation are presented. Ofher topics are treated in Ine same m e r .

The principal deficiency of image-based visual servoing is that the induced (3D) trajectories are not optimal and sometimes, especially when the displacement to realize is large, these trajectories are not physically valid leading to the failure of the servoing process. Furthermore, visual control needs a matching step between the features extracted from the initial image and the desired one. This step can be problematic (or impossible) when the camera displacement between the acquisitions of the initial and desired images is large and/or for complex scenes. To resolve these deficiencies, we couple an image interpolation process between N relay images extracted from a database and an image-based trajectory tracking. The camera calibration and the model of the observed scene are not assumed to be known. The relay images are interpolated in such a way that the corresponding camera trajectory is minimal. First a closed form collineation path is obtained and then the analytical form of image features trajectories are derived and efficiently tracked using a purely image-based control. Experimental results obtained on a six DOF eye-in-hand robotic system are presented and confirm the validity of the proposed approach.