An image processing method and device

Abstract

The disclosed image process method for obtaining more accurate noise standard deviation or variance comprises: blocking the image frame to obtain the standard deviation or variance of small block, or, blocking the frame with its neighbor reference frame to obtain standard deviation or variance of every difference block; counting number of the block with same standard deviation or variance, and determining the noise standard deviation or variance according to the standard deviation or variance of block with the greatest number.

Description

technical field [0001] The present invention relates to an image processing method and device. Background technique [0002] Generally, video images inevitably contain noise. Noise not only affects image quality intuitively, but also greatly degrades the performance of many video image processing algorithms. Therefore, general video processing equipment should have noise removal devices. The performance of the denoising algorithm depends to a large extent on the accurate known or accurate estimation of the noise variance or noise standard deviation, but generally speaking, the noise variance or noise standard deviation cannot be accurately known, so a simple and reliable noise estimation algorithm is designed. And the denoising algorithm based on this is of great significance to video image processing. [0003] For noise estimation, existing video sequence noise estimation methods can be divided into two types: intra-frame estimation and inter-frame estimation. For intra...

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Problems solved by technology

For intra-frame noise estimation methods, most methods use the assumption that there are a certain proportion or a certain number of uniform blocks in the frame, and use the assumed proportion or number of blocks as a certain criterion for the final noise estimation. The accuracy of α depends largely on whether the assumed value of the ratio or the number of blocks is accurate. If the assumed value is too small, the estimated noise variance or noise standard deviation is also small; if the assumed value is too large, the estimated The noise variance or noise standard deviation is also too large; and no matter how the assumption is taken, the method cannot accurately estimate the noise variance or noise standard deviation in a smooth image and a detailed image at the same time, that is to say, the estimation of the method The reliability of the result is low; similarly, for inter-frame noise estimation methods, most methods are also based on the assumption that there are a certain proportion or a certain number of still blocks between frames. For the same reason, this method also has the above limitations

[0004] Moreover, the existing methods for estimating the noise variance using only intra- or inter-frame information are not very robust, because the intra-frame estimation method relies on uniform blocks within the frame, and the inter-frame estimation method relies on For static blocks between frames, if a certain condition is not satisfied, the noise estimation method that relies on this condition is no longer applicable

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