%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2020/09.29.19.17
%2 sid.inpe.br/sibgrapi/2020/09.29.19.17.42
%@doi 10.1109/SIBGRAPI51738.2020.00020
%T Performance and error analysis of recursive edge-aware Gaussian filters on GPUs
%D 2020
%A Ferreira, Hermes H.,
%A Gastal, Eduardo S. L.,
%A Schnorr, Lucas M.,
%A Navaux, Philippe O. A.,
%@affiliation Institute of Mathematics and Statistics - UFRGS
%@affiliation Institute of Informatics - UFRGS
%@affiliation Institute of Informatics - UFRGS
%@affiliation Institute of Informatics - UFRGS
%E Musse, Soraia Raupp,
%E Cesar Junior, Roberto Marcondes,
%E Pelechano, Nuria,
%E Wang, Zhangyang (Atlas),
%B Conference on Graphics, Patterns and Images, 33 (SIBGRAPI)
%C Porto de Galinhas (virtual)
%8 7-10 Nov. 2020
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K edge-aware filtering, GPU processing, high-performance computing, recursive filtering, image processing.
%X We present a schematic for image edge-aware Gaussian GPU filtering which has linear complexity on the number of pixels of the image. It allows us to reduce the execution time as we increase the number of Streaming Multiprocessors (SMs) on the GPU. We make use of a domain transformation and use a complex-valued recursive formulation of the Gaussian filter. The algorithm partitions the image in disjoint regions, where we compute in parallel the filtering operations, avoiding communication between regions. Our implementation leads to a real-time solution using a modern GPU. With the RTX 2080 Ti, we achieved an execution time of less than 10 milliseconds for 2 filtering iterations on high-resolution RGB images of dimensions 2048x2048.
%@language en
%3 Ferreira_et_al_2020_Performance_edge-aware_filters_GPUs.pdf