FIELD OF INVENTION

The present invention relates to the field of image processing technology, and in particular, to an image adaptive noise reduction method and a device thereof.

BACKGROUND OF INVENTION

The block-based Discrete Cosine Transform (BDCT) coding is widely used in the compression field, including image and video compression standards, such as JPEG, H.264, etc. However, because BDCT ignores correlations between neighboring blocks, discontinuities may occur at block boundaries.

Technical Problem

Blocking and mosquito noise caused by traditional decompression use global noise reduction methods, such as bilateral filtering noise reduction methods with uniform parameters for an entire image. If the bilateral filtering noise reduction methods with uniform parameters are used for the image, regions with more details will still be blurred, and reducing image qualities. Moreover, due to limited hardware resources, complex algorithms will cause longer processing time and cannot be processed in real time.

TECHNICAL SOLUTION

In view of shortcomings of the above problems, an objective of the present invention is to provide a new technical solution to solve one of the above problems. The present invention provides a method based on the human eye significance analysis, which can reduce consumption of hardware resources to a certain extent when the human eye is difficult to detect, and obtains a detailed map through local entropy, which can retain details as much as possible while denoising.

In order to achieve the above objective, the present invention provides an image adaptive noise reduction method, comprising following steps of: (1) dividing an original RGB image with noise into a plurality of sub-blocks; (2) performing an image space conversion from an RGB space to a YCbCr space for all the sub-blocks; (3) performing a significance analysis on each of the sub-blocks to obtain a significant characteristic map after the image space conversion; (4) determining whether a weight threshold of the significant characteristic map of a currently processed one of the sub-blocks is greater than a significant standard value, wherein if the weight threshold is greater than the significant standard value, the currently processed one of the sub-blocks is classified into a significant characteristic region, otherwise currently processed one of the sub-blocks is classified into a non-significant characteristic region, wherein significant values of the sub-blocks are generated and recorded, and all the significant characteristic maps are examined to obtain the significant characteristic region and the non-significant characteristic region; (5) obtaining a detailed map through a local entropy calculation for each sub-block in the significant characteristic region; performing bilateral filtering on Y channels of all the sub-blocks in the significant characteristic region, and using the detail map to adaptively adjust bilateral filtering results to output first noise reduction images; adjusting channel values of the Y channels of all the sub-blocks in the significant characteristic region through the detail map to output second noise reduction images; mixing the first noise reduction image and the second noise reduction image to obtain a first image, each sub-block in the non-significant characteristic region outputs a second image with original pixel values, and mixing all the first images with the second images to obtain a mixed image; and (6) performing an image space inverse conversion from the YCbCr space to the RGB space for the mixed image and outputting a final image.

In order to achieve the above objective, the present invention further provides an image adaptive noise reduction method, comprising following steps of: (1) dividing an original RGB image with noise into a plurality of sub-blocks; (2) performing an image space conversion from an RGB space to a YCbCr space for all the sub-blocks; (3) performing a significance analysis on each of the sub-blocks to obtain a significant characteristic map after the image space conversion; (4) performing a threshold segmentation on all the significant characteristic maps by a significant standard value to obtain a significant characteristic region and a non-significant characteristic region; (5) performing adaptive noise reduction on pixel values of the sub-blocks in the significant characteristic region to output first images, and each the sub-block in the non-significant characteristic region outputs a second image with an original pixel value, and mixing all the first images with the second images to obtain a mixed image; and (6) performing an image space inverse conversion from the YCbCr space to the RGB space for the mixed image and outputting a final image.

In order to achieve the above objective, the present invention further provides an image adaptive noise reduction device, comprising: an image division module for dividing an original RGB image with noise into a plurality of sub-blocks; an image space conversion module for performing an image space conversion from an RGB space to a YCbCr space on all the sub-blocks; a significance analysis module for performing a significance analysis on each of the sub-blocks to obtain a significant characteristic map after the image space conversion; a significant segmentation module for performing a threshold segmentation on all the significant characteristic maps by a significant standard value to obtain a significant characteristic region and a non-significant characteristic region; an image output module for performing adaptive noise reduction on pixel values of the sub-blocks in the significant characteristic region to output first images, controlling each the sub-block in the non-significant characteristic region to output a second image with an original pixel value, and mixing the first image and the second image to obtain a mixed image; and an image space inverse conversion module for inversely converting the mixed image from the YCbCr space to the RGB space and outputting a final image.

BENEFICIAL EFFECT

The beneficial effect of the present invention is: using a method of dividing images, based on significant characteristics of the images, adaptively denoising a significant region and reducing a noise reduction of non-significant region. Without reducing perceived quality of people, while improving the image display quality, it saves algorithm running time and hardware resources. Calculating image detail map through local entropy, adaptively adjusting weight of bilateral filtering according to an amount of detail, retaining the details, and solving a phenomenon of blurring in the detail region caused by a filtering method to achieve a sufficient noise reduction effect. The significance analysis method of image segmentation can be applied to other noise reduction algorithms and has universality.

