Video Super-Resolution Algorithm Using Bi-Directional Overlapped Block Motion Compensation and On-the-Fly Dictionary Training

Song, Byung Cheol; Jeong, Shin-Cheol; Choi, Yanglim

doi:10.1109/tcsvt.2010.2087454

Cited by 60 publications

(57 citation statements)

References 17 publications

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“…For fusing the scaled LR images, different filters can be used, such as mean and median filters [125], [156], [157], [190], [216], [299], [479], adaptive normalized averaging [167], Adaboost classifier [364], and SVD-based filters [582]. These algorithms have been shown to be much faster than the IBP algorithms [ [58], [74], [115], [116], [124], [125], [156], [157], [226] (the last five are known as shift and add), [167], [176], [190], [299], [319], [364], [397], [479], [544], [546], [582] Non-parametric [418], [419], [426], [439], [514], [560], [563], [617] In [125], [156], [157], [216] it was shown that the median fusion of the LR images when they are registered, is equivalent to the ML estimation of the residual of the imaging model of Eq. (4) and results in a robust SR algorithm if the motion be...…”

Section: Direct Methodsmentioning

confidence: 99%

“…- [422], [480], [481], [482], [515], [537], [544], [547] where a reconstruction based SR is followed by a learning based SR.…”

Section: Combined Methodsmentioning

confidence: 99%

“…Each block in the reference image is then compared against every block in a neighborhood of blocks in the new image. Different search techniques are possible for finding the corresponding block of a reference block in the new image: Sum of Absolute Differences (SAD) [364], [369], [397], [542], [544], SSD [369], Sum of Absolute Transform Differences (SATD), Sum of Squared Transform Differences (SSTD) [282]. A comparison of these search techniques can be found in [333].…”

Section: Geometric Registrationmentioning

confidence: 99%

See 2 more Smart Citations

Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

588

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: Direct Methodsmentioning

confidence: 99%

“…- [422], [480], [481], [482], [515], [537], [544], [547] where a reconstruction based SR is followed by a learning based SR.…”

Section: Combined Methodsmentioning

confidence: 99%

Section: Geometric Registrationmentioning

confidence: 99%

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Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

588

292

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“…Therefore, at the boundaries between the interpolated occluded blocks and super resolved motion blocks, we can observe blocking artifacts, as the blocks are derived from different algorithms. To decrease the blocking artifacts, a smoothing filter is applied to the boundary pixels (Figure 4.8) [92]. This is done by averaging the pixels at the boundaries of the neighboring blocks.…”

Section: Post Processing Modulementioning

confidence: 99%

Motion block based video super resolution

Izadpanahi

Demirel

2013

Digital Signal Processing

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A multi-frame super resolution process can be used for enhancing the resolution of video frames by employing the information of consecutive low-resolution frames taken from almost the same scene. Most of these super resolution algorithms are only suitable for global motion model. Nevertheless, if a local motion pattern such as movements of some objects happens between the low resolution frames a global motion model cannot provide efficient performance. Considering this problem, we propose a novel super resolution framework, where the moving and static regions in video frames are processed separately. Occlusion is another issue, which is not considered in most of the video super resolution processes. This problem occurs when a new object appears or an object disappears in the video frames. The proposed motionblock based super resolution method not only offers a local motion model but also deals with the occluded areas in a proper way.This thesis presents a new video super resolution technique, based on the motion and static areas of the low resolution video frames. In order to separate the motion and static blocks, a block motion estimation method is employed between a reference and its neighbouring frames. Among the motion blocks, the occluded blocks are identified using an adaptive threshold applied on each block individually. Structure-adaptive normalized convolution (SANC) reconstruction method is used to generate the high resolution static and motion blocks where discrete wavelet transform (DWT) based interpolation is used to produce the high resolution occluded blocks. The static and motion blocks are combined into a high resolution frame.

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“…A wide variety of such methods have been proposed in the literature [2][3][4][5][6]. Most of these methods begin with registration of LR frames to a common HR reference grid.…”

Section: Introductionmentioning

confidence: 99%

A collaborative adaptive Wiener filter for multi-frame super-resolution

Mohamed

Hardie

2015

Front. Phys.

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Factors that can limit the effective resolution of an imaging system may include aliasing from under-sampling, blur from the optics and external factors, and sensor noise. Image restoration and super-resolution (SR) techniques can be used to improve image resolution. One SR method, developed recently, is the adaptive Wiener filter (AWF) SR algorithm. This is a multi-frame SR method that combines registered temporal frames through a joint nonuniform interpolation and restoration process to provide a high-resolution image estimate. Variations of this method have been demonstrated to be effective for multi-frame SR, as well demosaicing RGB and polarimetric imagery. While the AWF SR method effectively exploits subpixel shifts between temporal frames, it does not exploit self similarity within the observed imagery. However, very recently, the current authors have developed a multi-patch extension of the AWF method. This new method is referred to as a collaborative AWF (CAWF). The CAWF method employs a finite size moving window. At each position, we identify the most similar patches in the image within a given search window about the reference patch. A single-stage weighted sum of all of the pixels in all of the similar patches is used to estimate the center pixel in the reference patch. Like the AWF, the CAWF can perform nonuniform interpolation, deblurring, and denoising jointly. The big advantage of the CAWF, vs. the AWF, is the CAWF can also exploit self-similarity. This is particularly beneficial for treating low signal-to-noise ratio (SNR) imagery. To date, the CAWF has only been developed for Nyquist-sampled single-frame image restoration. In this paper, we extend the CAWF method for multi-frame SR. We provide a quantitative performance comparison between the CAWF SR and the AWF SR techniques using real and simulated data. We demonstrate that CAWF SR outperforms AWF SR, especially in low SNR applications.

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Video Super-Resolution Algorithm Using Bi-Directional Overlapped Block Motion Compensation and On-the-Fly Dictionary Training

Cited by 60 publications

References 17 publications

Super-resolution: a comprehensive survey

Super-resolution: a comprehensive survey

Motion block based video super resolution

A collaborative adaptive Wiener filter for multi-frame super-resolution

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