Digital image denoising method based on NSST and CNN

Abstract

The invention relates to a digital image denoising method based on NSST and CNN. The digital image denoising method comprises the following steps: 1, performing network training: converting noise approximately complying with Poisson distribution in an image into noise approximately complying with standard Gaussian distribution through Ascombe transformation; through the NSST, namely the non-subsampled shearlet transform, decomposing a noise image and a high-quality image corresponding thereto into multiple levels of sub-band images respectively, and shearing each level of sub-band image into image blocks with certain sizes as a data set; based on the obtained data set, performing convolutional neural network training, namely CNN training; 2, denoising the image based on a network model obtained in the step 1.

Description

technical field [0001] The invention relates to a digital image noise reduction method based on NSST and CNN. Background technique [0002] In the prior art, digital image noise reduction algorithms include methods based on non-local similarity theory, sparse representation and dictionary learning theory, and transformation filtering theory. The methods based on transform filtering theory mostly perform threshold filtering on the transform coefficients in the transform domain, and then inversely transform the filtering results to synthesize the final noise-reduced image. De-noising effect, it is difficult to retain more image detail information while removing noise. [0003] In recent years, convolutional neural network (CNN) has developed rapidly, and has made breakthrough research in image, speech and text recognition. Based on its powerful feature extraction ability, convolutional neural network can extract multiple feature maps from an input image, which is helpful for...

AI Technical Summary

Problems solved by technology

However, when the image domain convolutional neural network is used for digital image noise reduction, it will also produce excessive blurring, resulting in the loss of image detail information.

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