As the core parameter of the classical block-matching and 3D filtering (BM3D) algorithm,noise level (variance) needs to be manually set,which greatly affects the BM3D algorithm ′s noise reduction performance and limits its application.To resolve this probl em,a fast noise level estimation algorithm that utilizes the feature vectors based on the natural scene statistics and support vector regression (SVR ) techniques is proposed,based on which the standard BM3D algorithm is transformed into an adaptive denoising algorithm (adaptive BM3D). Specifically,the sub-band coefficients of an image obtained from a wavelet tra nsform over three scales and three orientations are parameterized using a generalized Gaussian distribution (GGD), and these estimated parameters are used to form a feature vector for describing image noise level of the image .Given a lot of feature vectors obtained from training noisy images,we utilize support vector regression (SVR) to train an estimation model to predict the noise level for any noisy image.Experimental results show that the actual image noise reduction capability of the proposed ABM3D algorithm is much better than that of the class ical BM3D algorithm,and it still maintains high efficiency,which gives it a sign ificant competitive edge compared with other existing state of the art alg orithms.