Abstract
Background: During the collection process, the prostate capsula is prone to introduce salt and pepper noise due to gastrointestinal peristalsis, which will affect the precision of subsequent object detection.
Objective: A cascade optimization scheme for image denoising based on image fusion was proposed to improve the peak signal-to-noise ratio (PSNR) and contour protection performance of heterogeneous medical images after image denoising.
Methods: Anisotropic diffusion fusion (ADF) was used to decompose the images denoised by adaptive median filter, non-local adaptive median filter and artificial neural network to generate the base layer and detail layer, which were fused by weighted average and Karhunen-Loeve Transform respectively. Finally, the image was reconstructed by linear superposition.
Results: Compared with the traditional denoising method, the image denoised by this method has a higher PSNR while maintaining the image edge contour.
Conclusion: Using the denoised dataset for object detection, the detection precision of the obtained model is higher.
