Image restoring method based on three dictionary block matching

Abstract

The invention discloses an image restoring method based on three dictionary block matching. The image restoring method mainly solves the problem that when an image is restored, the edge of the image cannot be sharpened, and the high-frequency details of part of images are lost in the prior art. The technical scheme of the image restoring method is as follows: firstly, a blurred image to be processed is input, a blur dictionary Db, a clear dictionary Dc and a high-frequency dictionary Dh are created; then the blurred image to be processed is subjected to image blocking, and the most matched image blocks of all the image blocks in the blur dictionary Db are found; a low-frequency result image and a high-frequency result image are restored according to the one-to-one correspondent relationship among three dictionaries; and finally adding the restored low-frequency result image with the restored high-frequency result image so that the final restored result image is obtained. According to the image restoring method disclosed by the invention, when the image is restored, the image gradient effect can be eliminated, the edge of the image is sharpened, the information of high-frequency details of the image is restored, and the restoration quality of the blurred image is improved. The image restoring method disclosed by the invention is suitable for restoring various blurred images of which the blurred types are known.

Description

technical field

[0001] The invention belongs to the technical field of image processing, in particular to a method for restoring blurred images, which can be used for restoring blurred images of various known blur types. Background technique

[0002] Image restoration refers to the removal or mitigation of image quality degradation in the process of acquiring digital images. It is an important and challenging research content in image processing. For the image restoration problem, researchers have proposed many methods.

[0003] Traditional restoration methods include inverse filtering, Wiener filtering, Kalman filtering and generalized inverse singular value decomposition, etc. These methods have been widely used in image restoration, but these methods require blurred images to have a high signal-to-noise ratio , methods such as inverse filtering are only suitable for images with high SNR, which limits the practical application of traditional restoration methods. Another ...

Examples

Embodiment Construction

[0032] refer to figure 1 , the specific implementation steps of the invention are as follows:

[0033] Step 1, input a blurred image X to be processed b , initialize the blurred image X b The low frequency result graph of X lis an empty matrix, i.e. X l = 0, initialize the blurred image X b The high-frequency result graph of X h is an empty matrix, i.e. X h =0, the low-frequency result map X l The matrix size and high frequency result plot of X h The size of the matrix is ​​the same as the blurred image X to be processed b are the same size.

[0034] Step 2, the blurred image X to be processed b Carry out a block with a size of 5×5, and overlap 4 pixels between adjacent blocks during the block process to obtain a set of image blocks P={P(i)|i=1, 2,...,G}, and initialize i= 1.

[0035] Step 3, assuming that the number of clear sample images used to construct the dictionary M=5, the fuzzy dictionary D b , clear dictionary D c and high frequency dictionary D h The ...

technical field

[0001] The invention belongs to the technical field of image processing, in particular to a method for restoring blurred images, which can be used for restoring blurred images of various known blur types. Background technique

[0002] Image restoration refers to the removal or mitigation of image quality degradation in the process of acquiring digital images. It is an important and challenging research content in image processing. For the image restoration problem, researchers have proposed many methods.

[0003] Traditional restoration methods include inverse filtering, Wiener filtering, Kalman filtering and generalized inverse singular value decomposition, etc. These methods have been widely used in image restoration, but these methods require blurred images to have a high signal-to-noise ratio , methods such as inverse filtering are only suitable for images with high SNR, which limits the practical application of traditional restoration methods. Another ...

Examples

Embodiment Construction

[0032] refer to figure 1 , the specific implementation steps of the invention are as follows:

[0033] Step 1, input a blurred image X to be processed b , initialize the blurred image X b The low frequency result graph of X lis an empty matrix, i.e. X l = 0, initialize the blurred image X b The high-frequency result graph of X h is an empty matrix, i.e. X h =0, the low-frequency result map X l The matrix size and high frequency result plot of X h The size of the matrix is ​​the same as the blurred image X to be processed b are the same size.

[0034] Step 2, the blurred image X to be processed b Carry out a block with a size of 5×5, and overlap 4 pixels between adjacent blocks during the block process to obtain a set of image blocks P={P(i)|i=1, 2,...,G}, and initialize i= 1.

[0035] Step 3, assuming that the number of clear sample images used to construct the dictionary M=5, the fuzzy dictionary D b , clear dictionary D c and high frequency dictionary D h The ...

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