Abstract: In the process of obtaining remote sensing images of mining areas,mixed noise (including additive Gaussian
white noise and multiplicative speckle noise) is widespread,seriously affecting the image quality and subsequent interpretation
and analysis. A denoising algorithm for remote sensing images of mining areas based on fractional differential in the wavelet domain
is proposed,aiming to effectively suppress mixed noise while preserving the edges and details of the image. The algorithm
first performs wavelet transform on the remote sensing image,decomposing it into low-frequency and high-frequency components,
where noise is mainly concentrated in the high-frequency components. A fractional differential operator with anisotropic
diffusion characteristics is applied to the high-frequency components,and by optimizing the fractional order parameters,the edges
and textures of the image are adaptively protected. The non-local means filtering algorithm is then used to further optimize
the high-frequency components processed by fractional differential,effectively suppressing the cumulative effect of noise during
the iterative process. The processed high-frequency components and the original low-frequency components are subjected to inverse
wavelet transform and reconstruction to obtain the denoised image. Experimental results show that the proposed algorithm
can effectively balance noise reduction and detail preservation while maintaining the edges and details of remote sensing images,
outperforming algorithms such as mean filtering,median filtering and non-local means filtering.