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.