Blurred image blind restoration method based on mixed type Markov expert field

Abstract

The invention discloses a blurred image blind restoration method based on a Gaussian scale mixed type Markov expert field. The method comprises the implementation steps that (1) modeling is carried out on noise, a restored image and a restored blurred kernel through a Gaussian model, the Gaussian scale mixed type Markov expert field and a sparse model based on an l1 norm respectively in a Bayes posterior probability model; (2) a Napierian logarithm is extracted from the obtained Bayes posterior probability model to obtain a problem to be optimized; (3) the restored image and the restored blurred kernel are initialized through a blurred image and a Gaussian blurred kernel respectively, and a maximum number of iterations is set; (4) in a certain iteration, the obtained restored blurred kernel is fixedly optimized, and the restored image is optimized; (5) the obtained restored image is fixedly optimized, and the restored blurred kernel is optimized; (6) if the number of iterations is smaller than the maximum number of iterations, the step (4) and the step (5) are repeatedly executed; (7) a regularization coefficient in the step (4) is adjusted, and the known blurred image is restored through the final restored blurred kernel obtained in the step (6). According to the method, the high-quality restored image can be obtained through a single blurred image.

Description

technical field [0001] The invention relates to computer image processing technology, in particular to a blurred image blind restoration method based on Gauss scale mixed Markov expert field. Background technique [0002] In the process of daily photography, astronomical observation or remote sensing ground imaging, if the lighting conditions are not ideal, it is necessary to increase the sensitivity (ISO) of the imaging device or extend the exposure time so that the imaging device can obtain sufficient exposure. However, these two methods usually have the following problems: increasing the ISO of the imaging device will cause a lot of noise in the obtained image, which affects the color and details of the image; The relative motion between the objects being photographed produces image blur. [0003] In order to solve the above problems, the usual method is to add image stabilization equipment to the camera. However, the image stabilization equipment is usually bulky or exp...

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Problems solved by technology

However, these two methods usually have the following problems: increasing the ISO of the imaging device will cause a lot of noise in the obtained image, which affects the color and details of the image; Relative motion between the objects being photographed, resulting in blurred images

[0005] Both the non-blind restoration method and the blind restoration method are typical ill-conditioned inverse problems, that is, a little noise in the blurred image will also introduce a lot of negative effects such as noise and ringing in the restoration result, which seriously affects the restoration result

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