A Video Deblurring Algorithm Based on Motion Vector and An Encorder-Decoder Network

Zhang, Shanqing; Li, Pengcheng; Yang, Meng; Li, Li; Zhou, Qili; Fu, Xiuwei

doi:10.1109/access.2019.2923759

Cited by 5 publications

(5 citation statements)

References 27 publications

(24 reference statements)

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“…We compare the proposed method with state-of-the-art methods [2], [4], [7], [16], [38]. Recent years has witnessed the rapid progress of deep learning methods in the field of low-level image processing, especially in image deblurring [38], [40], [41]. However, most of them directly map the blurred image to the deblurred one without estimating a kernel, which is one of the big differences between conventional optimization-based methods and deep learning methods.…”

Section: Resultsmentioning

confidence: 99%

Robust Motion Blur Kernel Estimation by Kernel Continuity Prior

et al. 2020

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The accurate kernel estimation is key to the blind motion deblurring. Many previous methods depend on the image regularization to recover strong edges in the observed image for kernel estimation. However, the estimated kernel will be degraded when recovered strong edges are less accurate, especially in images full of small-scale edges. Different from previous methods, we focus on the kernel regularization. Inspired by the fact that the blur kernel is highly related to the continuous camera motion trajectory during the image capturing, we propose to encourage the continuity of the kernel through a kernel prior. The proposed prior measures the continuity of each element in the kernel and generates a continuity map. By encouraging the sparsity of the map using L 0 norm, discontinuous kernel elements are suppressed. Since the model with the proposed prior is non-convex and non-linear, an approximation method is proposed to minimize the cost function efficiently. Numerous experimental results show that our method outperforms state-of-the-art methods on both the normal and challenging cases. Moreover, the proposed prior is able to further improve the performance of existing MAP-based methods. INDEX TERMS Image deblurring, robust blur Kernel estimation, small-scale edges, Kernel continuity.

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Section: Resultsmentioning

confidence: 99%

Robust Motion Blur Kernel Estimation by Kernel Continuity Prior

et al. 2020

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“…Methods of combining frame-byframe information can be divided into two groups: methods based on combining images to obtain a better representation of the object, and methods of combining extracted text recognition results. The first group includes methods for selecting the most informative frame [149], "super-resolution" methods that create a higher quality image based on several low-resolution frames [150], methods for tracking and combining images of a recognized object in a sequence of frames [151], methods of blur compensation by replacing blurred areas in one frame with their clearer versions taken from other frames or using deep learning techniques [152]. Also, data from various sensors of a mobile device, such as an accelerometer or gyroscope, can be used to better the recovery of a recognized document image.…”

Section: Video Sequence Recognitionmentioning

confidence: 99%

Document image analysis and recognition: a survey

et al. 2022

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This paper analyzes the problems of document image recognition and the existing solutions. Document recognition algorithms have been studied for quite a long time, but despite this, currently, the topic is relevant and research continues, as evidenced by a large number of associated publications and reviews. However, most of these works and reviews are devoted to individual recognition tasks. In this review, the entire set of methods, approaches, and algorithms necessary for document recognition is considered. A preliminary systematization allowed us to distinguish groups of methods for extracting information from documents of different types: single-page and multi-page, with text and handwritten contents, with a fixed template and flexible structure, and digitalized via different ways: scanning, photographing, video recording. Here, we consider methods of document recognition and analysis applied to a wide range of tasks: identification and verification of identity, due diligence, machine learning algorithms, questionnaires, and audits. The groups of methods necessary for the recognition of a single page image are examined: the classical computer vision algorithms, i.e., keypoints, local feature descriptors, Fast Hough Transforms, image binarization, and modern neural network models for document boundary detection, document classification, document structure analysis, i.e., text blocks and tables localization, extraction and recognition of the details, post-processing of recognition results. The review provides a description of publicly available experimental data packages for training and testing recognition algorithms. Methods for optimizing the performance of document image analysis and recognition methods are described.

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“…The methods of combining per-frame information can be divided into two groups: methods, relying on image combination to obtain a higher quality object representation, and methods of combining the extracted text recognition results. The first group includes methods for selecting the most informative frame [35,36], "superresolution" methods that create a higher quality image based on several low-resolution frames [37  39], methods for tracking and combining images of a recognized object on a sequence of frames [40,41], methods of blur compensation by replacing blurred areas in one frame with their clearer counterparts taken from other frames or using deep learning methods [42]. Also, for a better reconstruction of a recognized document image, it is possible to use the data obtained from various sensors of the recording device, such as, for example, an accelerometer or a gyroscope.…”

Section: Text Recognition In a Video Streammentioning

confidence: 99%

Weighted combination of per-frame recognition results for text recognition in a video stream

Petrova¹,

Bulatov

Arlazarov³

2021

Computer Optics

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The scope of uses of automated document recognition has extended and as a result, recognition techniques that do not require specialized equipment have become more relevant. Among such techniques, document recognition using mobile devices is of interest. However, it is not always possible to ensure controlled capturing conditions and, consequentially, high quality of input images. Unlike specialized scanners, mobile cameras allow using a video stream as an input, thus obtaining several images of the recognized object, captured with various characteristics. In this case, a problem of combining the information from multiple input frames arises. In this paper, we propose a weighing model for the process of combining the per-frame recognition results, two approaches to the weighted combination of the text recognition results, and two weighing criteria. The effectiveness of the proposed approaches is tested using datasets of identity documents captured with a mobile device camera in different conditions, including perspective distortion of the document image and low lighting conditions. The experimental results show that the weighting combination can improve the text recognition result quality in the video stream, and the per-character weighting method with input image focus estimation as a base criterion allows one to achieve the best results on the datasets analyzed.

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A Video Deblurring Algorithm Based on Motion Vector and An Encorder-Decoder Network

Cited by 5 publications

References 27 publications

Robust Motion Blur Kernel Estimation by Kernel Continuity Prior

Robust Motion Blur Kernel Estimation by Kernel Continuity Prior

Document image analysis and recognition: a survey

Weighted combination of per-frame recognition results for text recognition in a video stream

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