Image denoising method and device

Abstract

The invention provides an image denoising method and device. The method comprises determining denoising weight of each image region in a target image region set according to an average value of each color component and a reference color component value of each image region in the target image region set, denoising a first pixel point according to the denoising weight of each image region in the target image region set, better removing color noise of a flat region, and better keeping details. Simultaneously, the image denoising method and device can save a large quantity of operation resources. Particularly under the condition of a big image region, more operation resources are saved.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of image processing, and more specifically, to a method and device for image noise reduction. Background technique [0002] In order to make the image sensor only sensitive to one of the three colors of red, blue, and green, the image sensor generally uses a color filter array. Each pixel in an image file obtained by an image sensor using a color filter array has only one color component, and this image file is called a Bayer template. The final color picture needs to be processed based on the neighborhood point interpolation algorithm to obtain a color picture. The internal circuits of the sensor will inevitably interfere during the image capture process, which will cause noise interference in the image file obtained by the image sensor. Therefore, it is very necessary to use an algorithm to correct the Bayer template image data obtained by the image sensor. [0003] In an existing s...

AI Technical Summary

Problems solved by technology

The denoising result of this scheme on Bayer template image data is not ideal, because each pixel in each image area has only one color component, and an image area composed of a single color component cannot well identify the image The distribution of details in the area

Therefore, the result of denoising directly based on the Gaussian weighted Euclidean distance of each pixel is not accurate.

The phenomenon after denoising is that the color noise is often not removed cleanly in the flat area of ​​​​the image, and the blurring effect is brought about in the area with rich image details.

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