Image denoising method and device and terminal equipment

Abstract

The invention discloses an image denoising method and device and terminal equipment. The method comprises the steps that size information of a to-be-processed image is acquired; transforming referencefiltering parameters of a pre-obtained reference image based on a relative relation between the size information of the to-be-processed image and the size information of the pre-obtained reference image to obtain filtering parameters of the to-be-processed image; and denoising the to-be-processed image based on the filtering parameters of the to-be-processed image. In the embodiment of the specification, the invention also discloses a method for implementing the same, based on a relative relationship between the to-be-processed image and size information of a reference image obtained in advance, the filtering parameter of the reference image is transformed, and the transformed filtering parameter is used for denoising, so that adaptive optimization of the filtering parameter according tothe size information of the to-be-processed image is realized, the denoising effect of the image is ensured, and the operation is simplified.

Description

technical field [0001] This specification relates to the technical field of image processing, and in particular to an image denoising method, device and terminal equipment. Background technique [0002] Currently commonly used image denoising methods, such as mean filter, median filter, non-local mean filter, etc., mostly consider the similarity of pixel information in a certain area of ​​​​the image, and perform weighted processing on the pixels to weaken noise and optimize image quality. the goal of. [0003] When the filtering operation is performed, when the pixel size of the image changes, the similarity of the pixels contained in the selected neighborhood blocks will change, resulting in a decrease in the image denoising effect or excessive smoothing, so the parameters need to be readjusted. At present, the adjustment of image filtering parameters mostly depends on the actual effect and related parameters (such as root mean square error, structural similarity, etc.) t...

AI Technical Summary

Problems solved by technology

[0003] During the filtering operation, when the pixel size of the image changes, the similarity of the pixels contained in the selected neighborhood block will change, resulting in image loss The noise effect is reduced or excessive smoothing, etc., so the parameters need to be readjusted

At present, the adjustment of image filtering parameters mostly depends on the actual effect and related parameters (such as root mean square error, structural similarity, etc.) to measure and adjust, which usually requires repeated adjustment and optimization, which brings a lot of inconvenience to image denoising processing

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