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.
Gaussian scales mixture model Temporal-spatial model Curvelet transformation Motion compensation Weighted impact factor
Liu Jinping Gui Weihua Tang Zhaohui Mu Xuemin Zhu Jianyong
School of Information Science & Engineering , Central South University, Chansha 410083
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
4027-4032
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）