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Noise Removal from Remote Sensed Images by NonLocal Means with OpenCL Algorithm

Donatella Granata, Angelo Palombo, Federico Santini, Umberto Amato
Istituto per le Applicazioni del Calcolo ‘Mario Picone’, Consiglio Nazionale delle Ricerche, Via dei Taurini 19, 00185 Roma, Italy
Remote Sensing, Volume 12, Issue 3, 2020

@article{granata2020noise,

   title={Noise Removal from Remote Sensed Images by NonLocal Means with OpenCL Algorithm},

   author={Granata, Donatella and Palombo, Angelo and Santini, Federico and Amato, Umberto},

   journal={Remote Sensing},

   volume={12},

   number={3},

   pages={414},

   year={2020},

   publisher={Multidisciplinary Digital Publishing Institute}

}

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We introduce a multi-platform portable implementation of the NonLocal Means methodology aimed at noise removal from remotely sensed images. It is particularly suited for hyperspectral sensors for which real-time applications are not possible with only CPU based algorithms. In the last decades computational devices have usually been a compound of cross-vendor sets of specifications (heterogeneous system architecture) that bring together integrated central processing (CPUs) and graphics processor (GPUs) units. However, the lack of standardization resulted in most implementations being too specific to a given architecture, eliminating (or making extremely difficult) code re-usability across different platforms. In order to address this issue, we implement a multi option NonLocal Means algorithm developed using the Open Computing Language (OpenCL) applied to Hyperion hyperspectral images. Experimental results demonstrate the dramatic speed-up reached by the algorithm on GPU with respect to conventional serial algorithms on CPU and portability across different platforms. This makes accurate real time denoising of hyperspectral images feasible.
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