Acta Photonica Sinica, Volume. 48, Issue 9, 910003(2019)
High Density Mixed Noise Removal Algorithm Based on Gaussian Curvature Optimization and Nonsubsampled Shearlet Transform
In order to improve the observability of mine images corrupted by mixed noise, a highdensity mixed noise removal algorithm based on Gaussian curvature optimization and nonsubsampled shearlet transform was proposed. The local Gaussian curvature is used to optimize the mixed noise image to suppress the influence of salt & pepper noise on the noise distribution, which makes the mixed noise distribution approximate to a Gaussian noise distribution. Then, the nonsubsampled shearlet transform is used to decompose the image optimized by Gaussian curvature and implement adaptive hard threshold shrinkage to remove the Gaussian noise in the mixed noise. Finally, the local Gaussian curvature optimization and the nonsubsampled shearlet transform are executed iteratively to reduce the residual noise until the output image gradient energy satisfies the stop condition. Experiments show that the proposed algorithm can effectively remove the highdensity mixed noise composed of Gaussian noise and salt and pepper noise, and effectively suppress the PseudoGibbs phenomenon caused by shearlet transform denoising algorithms, and effectively reduce the noise of mine images.
Get Citation
Copy Citation Text
WANG Manli, TIAN Zijian, GUI Weifeng, WU Jun. High Density Mixed Noise Removal Algorithm Based on Gaussian Curvature Optimization and Nonsubsampled Shearlet Transform[J]. Acta Photonica Sinica, 2019, 48(9): 910003
Received: Apr. 30, 2019
Accepted: --
Published Online: Oct. 12, 2019
The Author Email: Manli WANG (wml920@163.com)