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Two-scale sparse representation-based color image noise reduction method

A sparse representation and color image technology, applied in the field of image processing, can solve the problems of poor adaptability of local image information, not suitable for real-time processing, easy to lose image detail processing speed, etc., and achieve the effect of increasing time complexity

Inactive Publication Date: 2012-05-23
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

[0009] (2) The dictionary trained with global pictures has poor adaptability to local image information
However, the processing time is greatly increased, which is not suitable for real-time processing
[0011] Therefore, when processing color images in the prior art, there are problems of easy loss of image details and slow processing speed

Method used

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  • Two-scale sparse representation-based color image noise reduction method
  • Two-scale sparse representation-based color image noise reduction method
  • Two-scale sparse representation-based color image noise reduction method

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Embodiment Construction

[0066] Embodiments of the present invention are described in detail below in conjunction with accompanying drawings:

[0067] Step 1. Use the K-SVD algorithm to train the dictionary for the entire image. For the image to be denoised with uniform content, the sparse representation effect is better; however, for the image to be denoised with rich content, the K-SVD algorithm tends to fall into local The minimum value makes the trained dictionary not the optimal dictionary. At the same time, experiments have shown that using small image blocks for the entire image is conducive to restoring image details, but it is easy to produce artificial blurring for large flat areas in the image; conversely, using large image blocks for the entire image is conducive to smoothing the image details. A large flat area, but it is easy to lose the detailed information in the image.

[0068] Therefore, the present invention selects a window with a size of n×n in advance, and uniformly divides the ...

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Abstract

The invention discloses a two-scale sparse representation-based color image noise reduction method, and belongs to the field of digital image processing. By the method, an image to be processed is partitioned according to the noise variance of the image, and different dictionaries are trained respectively in each area, so that the updated dictionary is matched with image information of the area well so as to obtain the better effect of image restoration; simultaneously, aiming at different influences of the size of image blocks on the quality of the noise-reduced image, calculation is performed by a weighting-based average algorithm and a two-scale method, so that each image block has the optimal sparse expression and reserve the details of the original image as many as possible while image noise is removed, and the time complexity of the algorithm is not increased.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to a color image noise reduction method based on two-scale sparse representation. Background technique [0002] Since the actual image will inevitably be disturbed by noise in the process of formation and transmission, it is of great practical significance to minimize the influence of noise on subsequent image processing. Image denoising is widely used in image processing, and its purpose is to improve the signal-to-noise ratio of the image, improve the image quality, and highlight the corresponding desired features. In recent years, in order to obtain a better image noise reduction effect, various methods have been explored. There are mainly mean filter, adaptive Wiener filter, median filter, morphological noise filter and wavelet denoising and so on. These methods all filter out the high-frequency components of the image. Although they can achieve the purpose of noise reduction, ...

Claims

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Application Information

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IPC IPC(8): G06T5/00
Inventor 郑智辉汪渤孙抗周志强董明杰石永生高志峰沈军
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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