Functional MRI is known to be prone to artifacts caused by spatio-temporally varying structural noise components such as gross head motion, CSF pulsation, physiological fluctuation, and magnetic susceptibility changes. The presence of these artifacts can cause negative and positive false activation and obscure true activated pixels. Thus the reliability of the functional images can be diminished. The authors have applied Bayesian image processing to reduce noise and artifacts in fMRI using local continuity information. The results indicate that Bayesian image processing reduces image-to-image signal fluctuation significantly, is effective in reducing fMRI noise and artifacts, and improves hue activity detection by enhancing the connectivity of the activated pixels.