The analysis of fluorescence recovery after photobleaching (FRAP) data is complicated by the measurement noise, inhomogeneous fluorescence distribution, and image movement during experiment. Conventionally, these issues are tackled by data preprocessing and averaging, which causes loss of quantitative properties. In this study, we present a method which automatically estimates and compensates both the movement and inhomogeneous fluorescence distribution within the data analysis. The method is based on modeling the raw FRAP data with a parametric matrix and searching for maximum likelihood parameters between the model and the data. The developed method also automatically estimates also the bleach profile, immobile fraction, and noise variance. Suitable numerical computational method was developed and implemented in a computer grid. Simulated and experimental FRAP data was created and analyzed to evaluate the method.