An image denoising method based on temporal-spatial information fusion for flotation froth images processing
is proposed. Firstly, a kind of classic image denosing method based on local spatial statistical model of Gaussian scale
mixture(GSM) and Bayes-least square inference is applied in the image Curvelet domain to estimate the optimal coefficients
based on the spatial domain information of single image. Then, weighted impact factors of the inter-frames according to
motion compensation and similarities measurement of the sub-blocks in the adjacent frames are introduced. Consequently,
ultimate and optimal image coefficients of the froth frame to be processed are computed by weighting the adjacent frames
coefficients according to the correlation information on the spatial-temporal domain with the weighted impact factors. The
experiment and application results explicitly show that this method can achieve much better performance to protect the bubble
details of the image sequences while removing the image noise, and it also achieves good results for the seriously polluted
image sequences.