(eng): The aim of this paper is to combine the strengths of two recently proposed Blind Source Separation~(BSS) algorithms. The first algorithm, abbreviated as EFICA, is a sophisticated variant of the well-known Independent Component Analysis~(ICA) algorithm FastICA. EFICA is based on minimizing the statistical dependencies between the instantaneous (marginal) distributions of the estimated source signals and therefore disregards any possible time structure of the sources. The second algorithm, WASOBI, is a weight-adjusted variant of SOBI, a popular BSS algorithm that uses only the time structure of the source signals to achieve the separation. The separation accuracy of EFICA and WASOBI can be assessed using the estimated source signals alone, therefore allowing us to choose the most appropriate of the two in every scenario. Here, two different EFICA-WASOBI combination approaches are proposed and their performance assessed using images and simulated signals.

(cze): Zamerem clanku je kombinace silnych stranek dvou nedavno navrzenych algoritmu pro slepou separaci signalu. Prvni algoritmus, znamy pod akronymem EFICA, je zalozen na minimalizaci statisticke nezavislosti rozlozeni jednotlivych zdroju v danem okamziku, ktery ignoruje moznou casovou strukturu signalu. Druhym signalem je algoritmus WASOBI, ktery vyuziva prave jen ruzne casove kovariancni struktury jednotlivych zdroju. Jelikoz presnost separace obou algoritmu lze odhadovat z danych dat, je mozne najit optimalni kombinaci techto algoritmu adaptivne. Dve mozne kombinace oubou algoritmu jsou studovany v simulacich na separaci linearni smesi ctyr obrazku.