Super Resolution of Images and Video

Katsaggelos, Aggelos K.; Molina, Rafael; Mateos, Javier

doi:10.1007/978-3-031-02243-2

Cited by 85 publications

(4 citation statements)

References 160 publications

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“…They may be due to a known or an unknown blurring function that leads to the consideration of deconvolution [9][10][11][12][13] and blind deconvolution [3,14] problems. They may also be due to the use of very low-resolution devices, which lead to the combination of several low-resolution images to obtain a high-resolution one, the so called, super-resolution problem [15,16] or to the utilization of highly compressed images, which suffer from compression artifacts [17]. These types of degradations must be removed before the images or video sequences are used for classification or decision making.…”

Section: State Of the Artmentioning

confidence: 99%

Novel Methods for Forensic Multimedia Data Analysis: Part I

Perner¹

2020

Digital Forensic Science

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The increased usage of digital media in daily life has resulted in the demand for novel multimedia data analysis techniques that can help to use these data for forensic purposes. Processing of such data for police investigation and as evidence in a court of law, such that data interpretation is reliable, trustworthy, and efficient in terms of human time and other resources required, will help greatly to speed up investigation and make investigation more effective. If such data are to be used as evidence in a court of law, techniques that can confirm origin and integrity are necessary. In this chapter, we are proposing a new concept for new multimedia processing techniques for varied multimedia sources. We describe the background and motivation for our work. The overall system architecture is explained. We present the data to be used. After a review of the state of the art of related work of the multimedia data we consider in this work, we describe the method and techniques we are developing that go beyond the state of the art. The work will be continued in a Chapter Part II of this topic.

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Section: State Of the Artmentioning

confidence: 99%

Novel Methods for Forensic Multimedia Data Analysis: Part I

Perner¹

2020

Digital Forensic Science

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“…For VSR, multi-frame approaches are more welcomed as they can utilize temporal relations. Previous approaches (Tsai and Huang 1984;Elad and Hel-Or 2001;Farsiu et al 2004;Katsaggelos, Molina, and Mateos 2007;Belekos, Galatsanos, and Katsaggelos 2010;Babacan, Molina, and Katsaggelos 2011;Liu and Sun 2011;Ma et al 2015) usually involve a few components for motion estimation on LR frames. These approaches often require users to manually tune motion estimation parameters for each input.…”

Section: Related Workmentioning

confidence: 99%

“…On the contrary, multi-frame approaches take the underlying relation of frames into account. Many previous works (Katsaggelos, Molina, and Mateos 2007;Babacan, Molina, and Katsaggelos 2011;Liu and Sun 2011;Ma et al 2015) regularize the LR to HR mapping via motion-related priors. For example, Babacan et al (2011) reconstructed HR videos subject to rotation and translation among LR frames.…”

Section: Introductionmentioning

confidence: 99%

Building an End-to-End Spatial-Temporal Convolutional Network for Video Super-Resolution

Guo

Chao

2017

AAAI

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We propose an end-to-end deep network for video super-resolution. Our network is composed of a spatial component that encodes intra-frame visual patterns, a temporal component that discovers inter-frame relations, and a reconstruction component that aggregates information to predict details. We make the spatial component deep, so that it can better leverage spatial redundancies for rebuilding high-frequency structures. We organize the temporal component in a bidirectional and multi-scale fashion, to better capture how frames change across time. The effectiveness of the proposed approach is highlighted on two datasets, where we observe substantial improvements relative to the state of the arts.

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“…Other vision technologies, such as super resolution [108], view synthesis [109], and 3D model reconstruction [110], can be possibly applied to a video surveillance system. However, most of these technologies are either based on undistorted video data, or are independent of the error control procedure at the transmitter.…”

Section: Video Analysismentioning

confidence: 99%

Wireless Video Surveillance: A Survey

Katsaggelos

et al. 2013

IEEE Access

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A wireless video surveillance system consists of three major components: 1) the video capture and preprocessing; 2) the video compression and transmission in wireless sensor networks; and 3) the video analysis at the receiving end. A myriad of research works have been dedicated to this field due to its increasing popularity in surveillance applications. This survey provides a comprehensive overview of existing state-of-the-art technologies developed for wireless video surveillance, based on the in-depth analysis of the requirements and challenges in current systems. Specifically, the physical network infrastructure for video transmission over wireless channel is analyzed. The representative technologies for video capture and preliminary vision tasks are summarized. For video compression and transmission over the wireless networks, the ultimate goal is to maximize the received video quality under the resource limitation. This is also the main focus of this survey. We classify different schemes into categories including unequal error protection, error resilience, scalable video coding, distributed video coding, and cross-layer control. Crosslayer control proves to be a desirable measure for system-level optimal resource allocation. At the receiver's end, the received video is further processed for higher-level vision tasks, and the security and privacy issues in surveillance applications are also discussed.INDEX TERMS Video surveillance, wireless sensor networks, multimedia communications, cross-layer control, video analysis.

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Super Resolution of Images and Video

Cited by 85 publications

References 160 publications

Novel Methods for Forensic Multimedia Data Analysis: Part I

Novel Methods for Forensic Multimedia Data Analysis: Part I

Building an End-to-End Spatial-Temporal Convolutional Network for Video Super-Resolution

Wireless Video Surveillance: A Survey

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