Super-resolution mosaicking of UAV surveillance video

Wang, Yi; Fevig, Ronald; Schultz, Richard R.

doi:10.1109/icip.2008.4711762

Cited by 30 publications

(26 citation statements)

References 5 publications

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“…- [370] Fourier description-based registration has been used. - [371], [422], [592], [615] Each motion model has its own pros and cons. The proper motion estimation method depends on the char-acteristics of the image, the motion's velocity, and the type of motion (local or global).…”

Section: Geometric Registrationmentioning

confidence: 99%

“…It is discussed in [371] (2008) that this a priori term generates saturated data if it is applied to Unmanned Aerial Vehicle (UAV) data. Therefore, it has been suggested to combine it with the Hubert function, resulting in the following Bilateral Total Variation Hubert (BTVH): .…”

Section: Bilateral Total Variation (Btv) [125] (2003) (mentioning

confidence: 99%

“…The latter solution increases the capacitance of the system, which slows down the charge transfer rate. Furthermore, the mentioned hardware-based Satellite and Aerial imaging [3], [5], [6], [8], [9], [10], [22], [25], [28], [41], [51], [52], [54], [55], [59], [70], [79], [96], [104], [107], [108], [109], [113], [114], [157], [159], [160], [161], [167], [175], [196], [203], [234], [247], [258], [290], [304], [306], [345], [352], [371], [598], [603], [606], [613] Medical Image Processing [15], [27], [95], [107], [108], …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

600

292

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Super-resolution, the process of obtaining one or more high-resolution images from one or more low-resolution observations, has been a very attractive research topic over the last two decades. It has found practical applications in many real world problems in different fields, from satellite and aerial imaging to medical image processing, to facial image analysis, text image analysis, sign and number plates reading, and biometrics recognition, to name a few. This has resulted in many research papers, each developing a new superresolution algorithm for a specific purpose. The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy. For each of the groups in the taxonomy, the basic concepts of the algorithms are first explained and then the paths through which each of these groups have evolved are given in detail, by mentioning the contributions of different authors to the basic concepts of each group. Furthermore, common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super-resolution algorithms, and the most commonly employed databases are discussed.

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Section: Geometric Registrationmentioning

confidence: 99%

Section: Bilateral Total Variation (Btv) [125] (2003) (mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

600

292

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“…However, since the camera is often attached to the belly or wing of the airplane, the recorded video is subject to flight vibrations and disturbances from the elements. Video quality from UAVs is often marred by noise, jitters, and blurry frames [7,8]. Again, super-resolution can be utilized in this case to improve video quality so that the UAV can make better decisions based on the video feeds.…”

Section: Introductionmentioning

confidence: 99%

Exploring super-resolution implementations across multiple platforms

Leung

Memik

2013

EURASIP J. Adv. Signal Process.

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The performance of many applications, such as video streaming, webcam conferencing, and aerial surveillance, all greatly depend on video quality. A major issue with higher quality video is that either more data bandwidth or storage resources must be dedicated for transferring or storing the video. However, if the low-resolution video is transferred or stored in order to conserve data bandwidth and storage space, super-resolution is a viable solution that can be applied afterwards on the receiving end to rectify the poor quality of the low-resolution video. Superresolution is an imaging technique that leverages motion blur and multiple low-resolution frames to construct a high-resolution frame. In our paper, we implement and analyze a super-resolution algorithm across multiple platforms ranging from purely hardware to purely software and even a mix of both hardware and software. More specifically, we examine the performance for a field-programmable gate array (FPGA) implementation on two different FPGAs, a software/hardware solution on a FPGA with a soft core processor, a general purpose graphics processing unit (GPGPU) implementation, and a MATLAB implementation. Overall, we found that the GPGPU provides the best overall performance with up to 29 FPS with 35 iterations of the super-resolution algorithm. A high-performance FPGA can have comparable performance and rival the GPGPU in some cases. One of the interesting results was that the hardware/software FPGA combination performed worse than the pure software implementation.

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“…The drawback of this method is that it requires that the motion vectors and homography must be highly accurate, which is very difficult for real surveillance videos from UAS. Wang, Fevig and Schultz [11] used the overlapped area within five consecutive frames from a video sequence. Then sparse matrices were applied to model the relationship between the LR and SR frames, which can be solved using maximum a posteriori estimation.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluations for Super-Resolution Mosaicing on UAS Surveillance Videos

Camargo¹,

Palaniappan

2013

International Journal of Advanced Robotic Systems

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Unmanned Aircraft Systems (UAS) have been widely applied for reconnaissance and surveillance by exploiting information collected from the digital imaging payload. The super-resolution (SR) mosaicing of low-resolution (LR) UAS surveillance video frames has become a critical requirement for UAS video processing and is important for further effective image understanding. In this paper we develop a novel super-resolution framework, which does not require the construction of sparse matrices. The proposed method implements image operations in the spatial domain and applies an iterated back-projection to construct super-resolution mosaics from the overlapping UAS surveillance video frames. The Steepest Descent method, the Conjugate Gradient method and the Levenberg-Marquardt algorithm are used to numerically solve the nonlinear optimization problem for estimating a super-resolution mosaic. A quantitative performance comparison in terms of computation time and visual quality of the superresolution mosaics through the three numerical techniques is presented.

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Super-resolution mosaicking of UAV surveillance video

Cited by 30 publications

References 5 publications

Super-resolution: a comprehensive survey

Super-resolution: a comprehensive survey

Exploring super-resolution implementations across multiple platforms

Performance Evaluations for Super-Resolution Mosaicing on UAS Surveillance Videos

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