Abstract
The imaging device is susceptible to factors such as the subject or the shooting environment when imaging, and complex variable blurring occurs in the final imaging. In most cases, we not only do not have the conditions to re-shoot a clear image but also do not know the specific parameters of the variable blur in advance. Therefore, the purpose of this study is to propose a motion blur fuzzy blind removal algorithm for character images based on gradient domain and depth learning. Deep learning is to learn the inherent laws and representation levels of sample data, and the information obtained during these learning processes is of great help to the interpretation of data such as text, images, and sounds. The algorithm used in this study is to preprocess the image by using guided filtering and L0 filtering and send the preprocessed gradient domain image block to the designed convolutional neural network for training. Extract the trained model parameters and realize the fuzzy kernel estimation and image. Image deblurring is performed using the TV regular term during image restoration. The experiment proves that the algorithm can effectively suppress the ringing effect and reduce the noise, and the motion blur effect is better. In this study, the MLP method, the edge detection method, and the proposed method are discussed, respectively. The PSNR values of the three motion blur removal methods are 26.49, 27.51, and 29.18, respectively. It can be seen that the motion blur removal method proposed in this study can effectively remove image motion blur.
Highlights
With the proliferation of highly developed network technologies, these imaging devices produce a large number of images at all times. ese images enrich and facilitate people’s lives and play a pivotal role in improving production quality, safeguarding property, and life safety [2]
In blind image deblurring, the blur kernel needs to be Scientific Programming estimated before restoring a clear image. e estimation of the blur kernel is one of the core problems of blind image deblurring, and it is a difficult problem in blind image deblurring
In order to overcome the limited training samples available in hyperspectral images (HSI), they proposed a simple data enhancement method, which effectively improved the HSI classification accuracy. They tested the proposed method and other three HSI classification methods based on deep learning on two realworld HSI datasets. e experimental results show that our DC-CNN-based method is much better than the latest method [18]
Summary
With the rapid development of science and technology, various photographic equipment have sneaked into all aspects of human life and have become a common existence in daily work and life. Compared with the deblurring problem under the condition of fuzzy kernel, blind deblurring is a serious illposed problem and a more challenging practical problem It has a wider application range [13], so the fuzzy blind removal technique is based on single image. E exploration of image deblurring technology, especially the exploration of image fuzzy blind removal technology with the above two problems of fuzzy kernel unknown and fuzzy kernel variable, has great theoretical research significance in academia and in society and has a very high practical application value [17]. (1) A blind image motion blur removal algorithm based on gradient domain and deep learning is proposed. An image motion blur blind removal algorithm based on gradient domain and depth learning is proposed, which provides relevant research work support for the following methods.
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