Enhancement and Edge-Preserving Denoising: An OpenCL-Based Approach for Remote Sensing Imagery
| Autor: | Omar Perez-Martinez, Ivan E. Villalon-Turrubiates, Roberto Carrasco Alvarez, Alejandro Castillo Atoche, Jaime Ortegon Aguilar, Javier Vázquez Castillo |
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| Rok vydání: | 2016 |
| Předmět: |
Atmospheric Science
Image fusion business.industry Computer science Noise reduction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies Image processing 02 engineering and technology Iterative reconstruction Image enhancement Robustness (computer science) Digital image processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence Computers in Earth Sciences Graphics business 021101 geological & geomatics engineering Remote sensing |
| Zdroj: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9:5482-5492 |
| ISSN: | 2151-1535 1939-1404 |
| Popis: | Image enhancement and edge-preserving denoising are relevant steps before classification or other postprocessing techniques for remote sensing images. However, multisensor array systems are able to simultaneously capture several low-resolution images from the same area on different wavelengths, forming a high spatial/spectral resolution image and raising a series of new challenges. In this paper, an open computing language based parallel implementation approach is presented for near real-time enhancement based on Bayesian maximum entropy (BME), as well as an edge-preserving denoising algorithm for remote sensing imagery, which uses the local linear Stein's unbiased risk estimate (LLSURE). BME was selected for its results on synthetic aperture radar image enhancement, whereas LLSURE has shown better noise removal properties than other commonly used methods. Within this context, image processing methods are algorithmically adapted via parallel computing techniques and efficiently implemented using CPUs and commodity graphics processing units (GPUs). Experimental results demonstrate the reduction of computational load of real-world image processing for near real-time GPU adapted implementation. |
| Databáze: | OpenAIRE |
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