Spatial-temporal method for image denoising based on BLS-GSM in Curvelet transformation

We propose an image sequences (video) denoising method based on image temporal-spatial GSM (Gaussian Mixture Scales) modeling in Curvelet transformation. Firstly, we construct the Bayesian Least Squared GSM (BLS-GSM) based image denoising model from single image and obtain the optimal coefficient estimation of the uncontaminated image coefficients based on this model in the curvelet domain. Then, we carry out a novel spatial-temporal joint based image noise removing method by combining the single image based denoising model with a weighted impact factor conducted on the sequential images based on the relativity of the image coefficients among the image sequences. This new image denoising method is capable of achieved higher reconstruction quality while protecting more image details. Experimental results from the real engineering application validate the effectiveness of our method from a series of froth image sequences processing.