Signal reconstruction from partial data for sensor array imaging applications

Yegnanarayana, B.; Mariadassou, C. P.; Saini, Priyanka

doi:10.1016/0165-1684(90)90036-x

Cited by 11 publications

(2 citation statements)

References 11 publications

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“…In the current implementation, half-hourly Meteosat-5 thermal IR imagery and the TRMM data are merged in the frequency domain using a spectral estimation approach adapted from ideas in digital image restoration practice (e.g., Yegnanarayana et al 1990;Diethorn and Munson 1991;Plevritis and Macovski 1995;Ferreira 1996;Strohmer 1997;Tsao 2001;Gonzalez and Woods 1992). Levizzani et al (1996) proposed to extrapolate microwave rainfall estimates by using the former to calibrate infrared instantaneous rain-rate estimates.…”

Section: Concept and Algorithm Formulationmentioning

confidence: 99%

A Space-Filling Algorithm to Extrapolate Narrow-Swath Instantaneous TRMM Microwave Rain-Rate Estimates Using Thermal IR Imagery

Barros

Tao

2008

Journal of Atmospheric and Oceanic Technology

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A space-filling algorithm (SFA) based on 2D spectral estimation techniques was developed to extrapolate the spatial domain of the narrow-swath near-instantaneous rain-rate estimates from Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI) using thermal infrared imagery (Meteosat-5) without making use of calibration or statistical fitting. A comparison against rain gauge observations and the original PR 2A25 and TMI 2A12 estimates in the central Himalayas during the monsoon season (June-September) over a 3-yr period of 1999-2001 was conducted to assess the algorithm's performance. Evaluation over the continental United States was conducted against the NCEP stage IV combined radar and gauge analysis for selected events. Overall, the extrapolated PR and TMI rainfall fields derived using SFA exhibit skill comparable to the original TRMM estimates. The results indicate that probability of detection and threat scores of the reconstructed products are significantly better than the original PR data at high-elevation stations (Ͼ2000 m) on mountain ridges, and specifically for rainfall rates exceeding 2-5 mm h Ϫ1 and for afternoon convection. For low-elevation stations located in steep narrow valleys, the performance varies from year to year and deteriorates strongly for light rainfall (false alarm rates significantly increase). A preliminary comparison with other satellite products (e.g., 3B42, a TRMM-adjusted merged infrared-based rainfall product) suggests that integrating this algorithm in currently existing operational multisensor algorithms has the potential to improve significantly spatial resolution, texture, and detection of rainfall, especially in mountainous regions, which present some of the greatest challenges in precipitation retrieval from satellites over land, and for hydrological operations during extreme events.

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Section: Concept and Algorithm Formulationmentioning

confidence: 99%

A Space-Filling Algorithm to Extrapolate Narrow-Swath Instantaneous TRMM Microwave Rain-Rate Estimates Using Thermal IR Imagery

Barros

Tao

2008

Journal of Atmospheric and Oceanic Technology

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“…However, irregularly sampled images can arise from motion or disparity compensation, such as in motion-compensated video coding, motion compensated video interpolation or disparity-compensated interpolation in stereoscopic images [9]. Irregularly spaced image data also occur frequently in areas such as remote sensing [11], medical imaging, oceanography and human retinal perception, [2,3]. For example, in human retinal perception, human photoreceptors are not regularly distributed but have a personal signature denoted by the random positioning of cells.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Operators for Direct Use on Irregular Image Data

Coleman

Scotney²

2005

Image Analysis and Processing – ICIAP 2005

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Abstract. Standard image processing algorithms for digital images require the availability of complete, and regularly sampled, image data. This means that irregular image data must undergo reconstruction to yield regular images to which the algorithms are then applied.The more successful image reconstruction techniques tend to be expensive to implement. Other simpler techniques, such as image interpolation, whilst cheaper, are usually not adequate to support subsequent reliable image processing. This paper presents a family of autonomous image processing operators constructed using the finite element framework that enable direct processing of irregular image data without the need for image reconstruction. The successful use of reduced data (as little as 10% of the original image) affords rapid, accurate, reliable, and computationally inexpensive image processing techniques.

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Multiscale Laplacian Operators for Feature Extraction on Irregularly Distributed 3-D Range Data

Suganthan

Coleman

Scotney

Lecture Notes in Computer Science

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Signal reconstruction from partial data for sensor array imaging applications

Cited by 11 publications

References 11 publications

A Space-Filling Algorithm to Extrapolate Narrow-Swath Instantaneous TRMM Microwave Rain-Rate Estimates Using Thermal IR Imagery

A Space-Filling Algorithm to Extrapolate Narrow-Swath Instantaneous TRMM Microwave Rain-Rate Estimates Using Thermal IR Imagery

Autonomous Operators for Direct Use on Irregular Image Data

Multiscale Laplacian Operators for Feature Extraction on Irregularly Distributed 3-D Range Data

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