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Full-Text Articles in Physical Sciences and Mathematics
Restoration And Reconstruction From Overlapping Images For Multi-Image Fusion, Stephen E. Reichenbach, Jing Li
Restoration And Reconstruction From Overlapping Images For Multi-Image Fusion, Stephen E. Reichenbach, Jing Li
Steve Reichenbach
This paper describes a technique for restoring and reconstructing a scene from overlapping images. In situations where there are multiple, overlapping images of the same scene, it may be desirable to create a single image that most closely approximates the scene, based on the data in all of the available images. For example, successive swaths acquired by NASA’s moderate imaging spectrometer (MODIS) will overlap, particularly at wide scan angles, creating a severe visual artifact in the output image. Resampling the overlapping swaths to produce a more accurate image on a uniform grid requires restoration and reconstruction. The one-pass restoration and …
Two-Dimensional Cubic Convolution, Stephen E. Reichenbach, Frank Geng
Two-Dimensional Cubic Convolution, Stephen E. Reichenbach, Frank Geng
Steve Reichenbach
This paper develops two-dimensional (2-D), nonseparable, piecewise cubic convolution (PCC) for image interpolation. Traditionally, PCC has been implemented based on a one-dimensional (1-D) derivation with a separable generalization to two dimensions. However, typical scenes and imaging systems are not separable, so the traditional approach is suboptimal. We develop a closed-form derivation for a two-parameter, 2-D PCC kernel with support [-2, 2] [-2, 2] that is constrained for continuity, smoothness, symmetry, and flat-field response. Our analyses using several image models, including Markov random fields, demonstrate that the 2-D PCC yields small improvements in interpolation fidelity over the traditional, separable approach. The …
Classification And Cluster Analysis Of Complex Time-Of-Flight Secondary Ion Mass Spectrometry For Biological Samples, Stephen E. Reichenbach, Xue Tian, Qingping Tao, Alex Henderson
Classification And Cluster Analysis Of Complex Time-Of-Flight Secondary Ion Mass Spectrometry For Biological Samples, Stephen E. Reichenbach, Xue Tian, Qingping Tao, Alex Henderson
Steve Reichenbach
Identifying and separating subtly different biological samples is one of the most critical tasks in biological analysis. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is becoming a popular and important technique in the analysis of biological samples, because it can detect molecular information and characterize chemical composition. ToF-SIMS spectra of biological samples are enormously complex with large mass ranges and many peaks. As a result the classification and cluster analysis are challenging. This study presents a new classification algorithm, the most similar neighbor with a probability-based spectrum similarity measure (MSN- PSSM), which uses all the information in the entire ToF- SIMS …
Restoration And Reconstruction Of Avhrr Images, Stephen E. Reichenbach, Daniel Kohler, Dennis Strelow
Restoration And Reconstruction Of Avhrr Images, Stephen E. Reichenbach, Daniel Kohler, Dennis Strelow
Steve Reichenbach
This paper describes the design of small convolution kernels for the restoration and reconstruction of Advanced Very High Resolution Radiometer (AVHRR) images. The kernels are small enough to be implemented efficiently by convolution, yet effectively correct degradations and increase apparent resolution. The kernel derivation is based on a comprehensive, end-to-end system model that accounts for scene statistics, image acquisition blur, sampling effects, sensor noise, and postfilter reconstruction. The design maximizes image fidelity subject to explicit constraints on the spatial support and resolution of the kernel. The kernels can be designed with h e r resolution than the image to perform …
Image Interpolation By Two-Dimensional Parametric Cubic Convolution, Jiazheng Shi, Stephen E. Reichenbach
Image Interpolation By Two-Dimensional Parametric Cubic Convolution, Jiazheng Shi, Stephen E. Reichenbach
Steve Reichenbach
Cubic convolution is a popular method for image interpolation. Traditionally, the piecewise-cubic kernel has been derived in one dimension with one parameter and applied to two-dimensional (2-D) images in a separable fashion. However, images typically are statistically nonseparable, which motivates this investigation of nonseparable cubic convolution. This paper derives two new nonseparable, 2-D cubic-convolution kernels. The first kernel, with three parameters (designated 2D-3PCC), is the most general 2-D, piecewise-cubic interpolator defined on [-2, 2] x [-2, 2] with constraints for biaxial symmetry, diagonal (or 90 rotational) symmetry, continuity, and smoothness. The second kernel, with five parameters (designated 2D-5PCC), relaxes the …
Smart Templates For Peak Pattern Matching With Comprehensive Two-Dimensional Liquid Chromatography, Stephen E. Reichenbach, Peter W. Carr, Dwight R. Stoll, Qingping Tao
Smart Templates For Peak Pattern Matching With Comprehensive Two-Dimensional Liquid Chromatography, Stephen E. Reichenbach, Peter W. Carr, Dwight R. Stoll, Qingping Tao
Steve Reichenbach
Comprehensive two-dimensional liquid chromatography (LC × LC) generates information-rich but complex peak patterns that require automated processing for rapid chemical identification and classification. This paper describes a powerful approach and specific methods for peak pattern matching to identify and classify constituent peaks in data from LC × LC and other multidimensional chemical separations. The approach records a prototypical pattern of peaks with retention times and associated metadata, such as chemical identities and classes, in a template. Then, the template pattern is matched to the detected peaks in subsequent data and the metadata are copied from the template to identify and …