—In this paper, we propose a generic multi-temporal SAR despeckling method to extend any single-image speckle reduction algorithm to multi-temporal stacks. Our method, RAtio-BAsed multi-temporal SAR despeckling (RABASAR), is based on ratios and fully exploits a " super-image " (i.e. temporal mean) in the process. The proposed approach can be divided into three steps: 1) calculation of the " super-image " through temporal averaging; 2) denoising the ratio images formed through dividing the noisy images by the " super-image " ; 3) computing denoised images by multiplying the denoised ratio images with the " super-image ". Thanks to the spatial stationarity improvement in the ratio images, denoising these ratio images with a speckle-reduction method is more effective than denoising the original multi-temporal stack. The data volume to be processed is also reduced compared to other methods through the use of the " super-image ". The comparison with several state-of-the-art reference methods shows numerically (peak signal-noise-ratio, structure similarity index) and visually better results both on simulated and real SAR stacks. The proposed ratio-based denoising framework successfully extends single-image SAR denoising methods in order to exploit the temporal information of a time series.