High resolution images are often required in applications such as remote sensing, frame freeze in video, military and medical imaging. Digital image sensor arrays, which are used for image acquisition in many imaging systems, are not dense enough to prevent aliasing, so the acquired images will be degraded by aliasing effects. To prevent aliasing without loss of resolution, a dense detector array is required. But it may be very costly or unavailable, thus, many imaging systems are designed to allow some level of aliasing during image acquisition. The purpose of our work is to reconstruct an unaliased high resolution image from the acquired aliased image sequence. In this paper, we propose a spatially adaptive regularized iterative high resolution image reconstruction algorithm for blurred, noisy and down-sampled image sequences. The proposed approach is based on a Constrained Least Squares (CLS) high resolution reconstruction algorithm, with spatially adaptive regularization operators and parameters. These regularization terms are shown to improve the reconstructed image quality by forcing smoothness, while preserving edges in the reconstructed high resolution image. Accurate sub-pixel motion registration is the key of the success of the high resolution image reconstruction algorithm. However, sub-pixel motion registration may have some level of registration error. Therefore, a reconstruction algorithm which is robust against the registration error is required. The registration algorithm uses a gradient based sub-pixel motion estimator which provides shift information for each of the recorded frames. The proposed algorithm is based on a technique of high resolution image reconstruction, and it solves spatially adaptive regularized constrained least squre minimization functionals. In this paper, We show that the reconstruction algorithm gives dramatic improvements in the resolution of the reconstructed image and is effective in handling the aliased information. The proposed algorithm is also shown to be robust in the presence of severe registration error. Experimental results are provided to illustrate the performance of the proposed reconstruction algorithm. Comparative analyses with other reconstruction methods are also provided.
The chemical durability of a typical low k LTCC material consisting of zinc calcium aluminoborosilicate glass and alumina filler was studied. Leaching experiments were performed by measuring weight loss of the samples in selected acid and base aqueous solutions of HCl, H2SO4 and KOH at two different temperatures of 25° and 80°C. The leaching performance was found to depend strongly on the type of leaching solution, duration and temperature. The LTCC sample was most stable in 10 vol% KOH with a minimal weight loss of ~0.43% even after 300 min at 80°C. At the same condition, both cases of 10 vol% HCl and 10 vol% H2SO4 exhibited serious weight losses greater than 30%. It was interesting to observe that the high capability of leaching in the acid solutions originated significantly from the leaching of crystalline anorthite (CaAl2Si2O8) phase.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.