Research on Anisotropic Diffusion Wavelet Image Denoising Algorithm Based on Differential Curvature

In modified uniformly redundant arrays code imaging of gamma radiation internal contamination, in order to further decrease reconstruction artifacts with Maximum Likelihood Expectation Maximisation(MLEM) algorithm and to improve the efficiency of reconstruction, this paper proposed an anisotropic diffusion wavelet image denoising algorithm based on differential curvature (ADWIDDC algorithm), and combining with MLEM algorithm, a modified code imaging MLEM-ADWIDDC algorithm was obtained. Firstly, the reconstruction of the projection data obtained from the detector was conducted with MLEM algorithm. Secondly, the near-field artifacts were decreased with complementary imaging method. Finally, the image artifacts were further reduced by ADWIDDC algorithm. The simulation results show that the MLEM-ADWIDDC algorithm can better remove the artifacts and the texture of the reconstructed gamma source image is clearer when ¹³¹I source is designed as circular distribution, with the cut-off time of 195 s for the reconstruction algorithm to run. When ¹³¹I source is designed as “CDUT” distribution and the signal-to-noise ratio is set 5.10 dB as cut-off condition of code imaging reconstruction, the real reconstruction time of the MLEM-ADWIDDC algorithm is 98.36 s, which is 49% shorter than that of MLEM algorithm alone.