Electronic images are easily contaminated and blurred during the processes of acquisition, transmission, and storage, and the fidelity of the original images are degraded. To address this issue, a hybrid denoising method based upon variational mode decomposition (VMD) and Stein unbiased risk estimator (SURE) was proposed, taking the electron backscatter diffraction (EBSD) images of Aluminum alloy, dual-phase steel, and Ti6Al4V as examples. To begin with, the clean EBSD images were contaminated by adding Gaussian noise and speckle patterns noise. Then, the noisy image was decomposed into characteristic information component and high-frequency noise components using the Bi-variational mode decomposition (BVMD) algorithm. Next, the generated inherent characteristic component was fed into the Haar wavelet redundant dictionary (HWRD) for sparse representation. Meanwhile, the optimal objective shrinkage function was derived with the function of one-order differentiable shrinkage. Finally, the adaptive threshold was obtained via golden section search (GSS) method. Experimental results show that the proposed method could effectively remove the external interference noise and improve the peak signal-to-noise ratio (PSNR) of the image. Specifically, taking the EBSD image of Aluminum alloy as an example, when the noise standard deviation was 30, the proposed method exceeded the maximum point of single sparse SURE method in terms of PSNR value, which also outperformed the Neigh-Shrink method by 0.39 dB, the K-singular value decomposition (KSVD) method by 2.895 dB, and the soft-wavelet threshold denoising (SWTD) by 3.07 dB.