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Presentation of a method for removal of motion blur effect in images by using GAN

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Abstract

Blur is one of the common types of damage in the image. Image de-blurring is one of the biggest and most common challenges in the field of image processing. In this article, we present a suitable method for the caused blind blur removal by the motion in the image, in which, in addition to the original image recovery, we recover the kernel of the blur, which is the unknown. The blur kernel is a function that describes the amount and type of blur caused by imaging a point source of light. In our article, the end-to-end learning method for motion blur removal is presented. The presented learning in this paper is the basis of the content loss and the conditional generative adversarial networks. This method gets advanced efficiency in terms of the appearance of the visual and the similarity of the structure. This approach is faster than five times over similar methods. Also, we introduce a new method for artificial motion blur image generation from sharp images that provides the possibility of the increase of the real data. Although the focus of this article is on the removal of the caused blur by the motion on the natural images, our presented approach is capable of removing blur on text images as well, and it partially covers the blur that varies with the location. According to the comparison of existing approaches and the conducted tests, our presented approach has the greatest image output quality value in terms of peak signal-to-noise ratio parameters.

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Abbreviations

\({L}_{G}\) :

The combination loss

\({L}_{{G}_{\mathrm{adv}}}\) :

The adversarial loss function

\(\varphi .\gamma\) :

The impact factors

\({L}_{OCD}\) :

The training loss function

\({L}_{\mathrm{content}}\) :

The function of the content loss

\({G}_{\theta }({I}^{B})\) :

The image of the produced sharp from a blurred image \({I}^{B}\)

\({W}_{i.j}.{H}_{i.j}\) :

The specification mappings dimensions

\({\varphi }_{i.j}\) :

A specifications mapping that is got with the layer of the complexity \(j\)

\({I}^{S}.{I}^{B}\) :

The main hidden sharp images

\({D}_{w}\) :

The separator by the parameters of the weight \(w\) which should be learned

\(E\) :

The operator of the expectation

\(\widehat{I}\) :

The original image

\({G}_{\theta }\) :

A generator by the parameters of the weight \(\theta\)

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Funding

Anhui Provincial Natural Science Fund Project: Construction and Research of Text Sentiment Analysis Model Based on Deep Learning—Taking the Sentiment Orientation of Stock News as an Example (KJ2021A1531).

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  1. School of Information Engineering, Anhui International Business Vocational College, Hefei, 231131, Anhui, China

    Hu Jun

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Correspondence to Hu Jun.

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Jun, H. Presentation of a method for removal of motion blur effect in images by using GAN. J Opt (2023). https://doi.org/10.1007/s12596-023-01408-2

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