A Survey on the methods of Super-resolution Image Reconstruction

Babu, R. Sudheer; Murthy, K. E. Sreenivasa

doi:10.5120/1923-2568

Cited by 15 publications

(5 citation statements)

References 22 publications

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“…The applications of the topics presented throughout this survey are numerous, ranging from medical imaging to lossy image compression. Many systematic approaches on the topic have been proposed in [1][2][3]13,20], to name a few. Although its roots lie in the digital signal processing, the theory of digital image interpolation approaches towards resampling of 2D signals have evolved well beyond.…”

Section: Discussionmentioning

confidence: 99%

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A Study of Image Upsampling and Downsampling Filters

Dumitrescu¹,

Boiangiu²

2019

Computers

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In this paper, a set of techniques used for downsampling and upsampling of 2D images is analyzed on various image datasets. The comparison takes into account a significant number of interpolation kernels, their parameters, and their algebraical form, focusing mostly on linear interpolation methods with symmetric kernels. The most suitable metrics for measuring the performance of upsampling and downsampling filters’ combinations are presented, discussing their strengths and weaknesses. A test benchmark is proposed, and the obtained results are analyzed with respect to the presented metrics, offering explanations about specific filter behaviors in general, or just in certain circumstances. In the end, a set of filters and parameters recommendations is offered based on extensive testing on carefully selected image datasets. The entire research is based on the study of a large set of research papers and on a solid discussion of the underlying signal processing theory.

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Section: Discussionmentioning

confidence: 99%

“…Sudheer [20] presents a survey comprising multiple approaches towards image interpolation while keeping in mind the linear kernel-based approach and describing several other non-linear techniques for obtaining a super-resolution image from a normal resolution one.…”

Section: Introductionmentioning

confidence: 99%

A Study of Image Upsampling and Downsampling Filters

Dumitrescu¹,

Boiangiu²

2019

Computers

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“…Since Tsai and Huang first proposed the principle of super-resolution (SR) image reconstruction, many reconstruction methods have been proposed [36][37][38][39][40]. SR image reconstruction seeks resolution enhancement through the super-position of multiple low resolution (LR) images from slightly different perspectives.…”

Section: Introductionmentioning

confidence: 99%

Super-resolution imaging in a multiple layer EPID

Chen¹,

Rottmann²,

Yip³

et al. 2017

Biomed. Phys. Eng. Express

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A new portal imager consisting of four vertically stacked conventional electronic portal imaging device (EPID) layers has been constructed in pursuit of improved detective quantum efficiency (DQE). We hypothesize that super-resolution (SR) imaging can also be achieved in such a system by shifting each layer laterally by half a pixel relative to the layer above. Super-resolution imaging will improve resolution and contrast-to-noise ratio (CNR) in megavoltage (MV) planar and cone beam computed tomography (MV-CBCT) applications. Simulations are carried out to test this hypothesis with digital phantoms. To assess planar resolution, 2 mm long iron rods with 0.3 × 0.3 mm2 square cross-section are arranged in a grid pattern at the center of a 1 cm thick solid water. For measuring CNR in MV-CBCT, a 20 cm diameter digital phantom with 8 inserts of different electron densities is used. For measuring resolution in MV-CBCT, a digital phantom featuring a bar pattern similar to the Gammex™ phantom is used. A 6 MV beam is attenuated through each phantom and detected by each of the four detector layers. Fill factor of the detector is explicitly considered. Projections are blurred with an estimated point spread function (PSF) before super-resolution reconstruction. When projections from multiple shifted layers are used in SR reconstruction, even a simple shift-add fusion can significantly improve the resolution in reconstructed images. In the reconstructed planar image, the grid pattern becomes visually clearer. In MV-CBCT, combining projections from multiple layers results in increased CNR and resolution. The inclusion of two, three and four layers increases CNR by 40%, 70% and 99%, respectively. Shifting adjacent layers by half a pixel almost doubles resolution. In comparison, using four perfectly aligned layers does not improve resolution relative to a single layer.

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“…Because of problems associated with frequency domain based SR formulations (Babu & Murthy, 2011), we chose to work on a spatial domain based approach. To evaluate the efficienty of this super-resolution mosaicing algorithm, several new performance measurement metrics are used.…”

Section: Introductionmentioning

confidence: 99%

Robust Spatial-Domain Based Super-Resolution Mosaicing of CubeSat Video Frames: Algorithm and Evaluation

Ghosh¹,

Kaabouch²,

Fevig³

2014

CIS

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In this paper, a spatial domain based super-resolution mosaicing system and its evaluation results are presented. This algorithm incorporates two main algorithms, an image mosaicing algorithm and a super-resolution-reconstruction algorithm. Huber-based prior information is used to address the ill-posed nature of the super resolution problem. To test the efficiency of the proposed algorithm, four performance metrics are used: mean square error, peak signal-to-noise ratio, singular value decomposition based measure, and cumulative probability of blur detection. Testing is performed using datasets generated from both a high altitude balloon and an Unmanned Aerial Vehicle (UAV). Results show that the proposed algorithm is highly efficient in real-time applications, such as remote sensing on a small satellite (i.e., CubeSat.) Furthermore, the performance metrics are proven to be accurate in quantitative evaluation.

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A Survey on the methods of Super-resolution Image Reconstruction

Cited by 15 publications

References 22 publications

A Study of Image Upsampling and Downsampling Filters

A Study of Image Upsampling and Downsampling Filters

Super-resolution imaging in a multiple layer EPID

Robust Spatial-Domain Based Super-Resolution Mosaicing of CubeSat Video Frames: Algorithm and Evaluation

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