In order to improve the observability of mine images corrupted by mixed noise, a highdensity mixed noise removal algorithm based on Gaussian curvature optimization and nonsubsampled 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 nonsubsampled 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 nonsubsampled 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 highdensity mixed noise composed of Gaussian noise and salt and pepper noise, and effectively suppress the PseudoGibbs phenomenon caused by shearlet transform denoising algorithms, and effectively reduce the noise of mine images.