Image Noise Removal on Heterogeneous CPU-GPU Configurations

A parallel algorithm to remove impulsive noise in digital images using heterogeneous CPU/GPU computing is proposed. The parallel denoising algorithm is based on the peer group concept and uses an Euclidean metric. In order to identify the amount of pixels to be allocated in multi-core and GPUs, a performance analysis using large images is presented. A comparison of the parallel implementation in multi-core, GPUs and a combination of both is performed. Performance has been evaluated in terms of execution time and Megapixels/second. We present several optimization strategies especially effective for the multi-core environment, and demonstrate significant performance improvements. The main advantage of the proposed noise removal methodology is its computational speed, which enables efficient filtering of color images in real-time applications.

Optimização de um sistema híbrido off-grid PV, Gerador Diesel e Baterias

Tese de mestrado integrado, Engenharia da Energia e do Ambiente, Universidade de Lisboa, Faculdade de Ciências, 2016

Passenger vehicle, light truck and van convex mirror optimization and evaluation studies. Volume 2: evaluation of alternative mirror configurations. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Analysis and forecast of truck traffic loads and the relative damage to pavement systems as a function of axle configurations. Final report.

Texas State Department of Highways and Public Transportation, Transportation Planning Division, Austin

Administrator's guide to speed enforcement/patrol configurations for the national maximum speed limit (NMSL). Final report.

National Highway Traffic Safety Administration, Washington, D.C.

An approach to the compilation of array expressions in the OL/2 language.

Originally presented as the author's thesis, University of Illinois at Urbana-Champaign.