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Blind image deblurring method based on depth prior

A deblurring and image technology, applied in image enhancement, image data processing, biological neural network models, etc., can solve the problem that mathematical expressions are difficult to express the natural image prior, and achieve the effect of suppressing noise

Pending Publication Date: 2022-04-29
BEIJING UNIV OF TECH
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Problems solved by technology

Traditional priors mathematically model the statistical properties of natural images, but mathematical expressions are difficult to represent complex natural image priors

Method used

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  • Blind image deblurring method based on depth prior
  • Blind image deblurring method based on depth prior
  • Blind image deblurring method based on depth prior

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Embodiment Construction

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0041] In the image deblurring problem, the degradation process of a uniformly blurred image can be expressed as the following convolution form:

[0042] y=h*x+n (1) In the formula, y is a blurred image, h is a blur kernel, x is a clear image, n is noise, and * is a two-dimensional convolution operation. Under the convolutional model, the blind image deblurring method is to study how to simultaneously estimate the blur kernel h and the clear imag...

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Abstract

The invention discloses a blind image deblurring method based on depth prior. A deep convolutional neural network DIP-Net is used for implicitly modeling an image smoothness prior constraint to generate a clear image; estimating a fuzzy kernel by solving an accurate solution about a fuzzy kernel optimization problem; and alternately iteratively updating the blurring kernel and the clear image, calculating a loss function by using the restored clear image and the blurring kernel, and updating network parameters. Carrying out joint modeling on the blurred image and the blurred kernel, and simultaneously estimating a clear image and the blurred kernel by adopting a mode of alternately iterating a network model and a mathematical model; blind deblurring of end-to-end self-supervised learning is achieved only using blurred images without any additional implicit or explicit image priors. The regularization method is realized in combination with a deep network structure, and a blurred image and a blurred kernel truth value training network do not need to be used; compared with a traditional model method, the method does not need to employ an image pyramid mode to estimate the blurring kernel from coarse to fine, and effectively inhibits the noise in the restored image.

Description

technical field [0001] The present invention relates to the field of image deblurring, and more specifically, relates to a blind image deblurring method based on depth prior. Background technique [0002] During the image acquisition process, due to the influence of factors such as atmospheric turbulence, relative motion between the imaging device and the target, and inaccurate focusing of the imaging device, the acquired image will be blurred to a certain extent. In many fields such as traffic monitoring, biomedicine, astronomical observation, remote sensing and telemetry, clear images can provide more useful information. In order to meet the needs of clear images in various application fields, we generally start from two aspects of hardware and software. There are problems such as high cost, high technical difficulty, and susceptibility to environmental influences by improving hardware, while image deblurring technology refers to recovering clear images from blurred image...

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Application Information

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IPC IPC(8): G06T5/00G06N3/04
CPCG06N3/045G06T5/73Y02T10/40
Inventor 肖创柏王晓宁郭乐宁禹晶
Owner BEIJING UNIV OF TECH
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