Toeplitz-based iterative image fusion scheme for MRI

Malczewski, Krzysztof; Stasinski, Ryszard

doi:10.1109/icip.2008.4711761

Cited by 18 publications

(12 citation statements)

References 6 publications

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“…Global motion is a result of motion of the object and/or camera, while local motion is due to the non-rigid nature of the object, e.g., the human face, or due to imaging condition, e.g., the effect of hot air [363]. Global motion can be modeled by: -a translational model (which is common in satellite imaging) [170], [175], [181], [183], [217], [231], [397] -an affine model [20], [55], [195], [291], [293], [351], [425], [431], [483], [540] or -a projective model [84], [85], [86], [216], [316], [490], while local motion is modeled by a non-rigid motion model [363]. In a typical non-rigid motion model, a set of control points on a given image is usually combined using a weighting system to represent the positional information both in the reference image and in the new image to be registered with the reference image.…”

Section: Geometric Registrationmentioning

confidence: 99%

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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%

“…Another group of iterative methods are those based on the concept of POCS [7] [49], [53], [72], [77], [93], [103], [122], [155], [175], [184], [204], [243], [351]. These algorithms define an implicit cost function for solving the SR problem [155].…”

Section: Projection Onto Convex Setsmentioning

confidence: 99%

Super-resolution: a comprehensive survey

Nasrollahi

Moeslund

2014

Machine Vision and Applications

600

292

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show abstract

“…They showed that SR, using multiple offset images of the same scene and a proper registration, can produce better HR images compared with spline interpolation. Since then, SR methods have become common practice for many applications in different fields, such as remote sensing, 9,10 surveillance video, [11][12][13][14] medical imaging such as ultrasound, magnetic resonance imaging, and computerized tomography scan, [15][16][17][18] optical character recognition problems, and face recognition. [19][20][21][22][23] Different techniques have been developed for performing SR. 2,24,25 One such technique is based on sparse representation.…”

Section: Introductionmentioning

confidence: 99%

Reducible dictionaries for single image super-resolution based on patch matching and mean shifting

Rasti

Nasrollahi

Orlova

et al. 2017

J. Electron. Imaging

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Abstract. A single-image super-resolution (SR) method is proposed. The proposed method uses a generated dictionary from pairs of high resolution (HR) images and their corresponding low resolution (LR) representations. First, HR images and the corresponding LR ones are divided into patches of HR and LR, respectively, and then they are collected into separate dictionaries. Afterward, when performing SR, the distance between every patch of the input LR image and those of available LR patches in the LR dictionary is calculated. The minimum distance between the input LR patch and those in the LR dictionary is taken, and its counterpart from the HR dictionary is passed through an illumination enhancement process. By this technique, the noticeable change of illumination between neighbor patches in the super-resolved image is significantly reduced. The enhanced HR patch represents the HR patch of the super-resolved image. Finally, to remove the blocking effect caused by merging the patches, an average of the obtained HR image and the interpolated image obtained using bicubic interpolation is calculated. The quantitative and qualitative analyses show the superiority of the proposed technique over the conventional and state-of-art methods.

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“…The application of SR techniques covers a wide range of purposes such as Surveillance video [15][16], remote sensing [17], Medical imaging such as Computerized Tomography (CT) scan, Magnetic Resonance Imaging (MRI), Ultrasound [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Image resolution enhancement by using interpolation followed by iterative back projection

Rasti

Demirel

Anbarjafari³

2013

2013 21st Signal Processing and Communications Applications Conference (SIU)

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Abstract-In this paper, we propose a new super resolution technique based on the interpolation followed by registering them using iterative back projection (IBP). Low resolution images are being interpolated and then the interpolated images are being registered in order to generate a sharper high resolution image. The proposed technique has been tested on Lena, Elaine, Pepper, and Baboon. The quantitative peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) results as well as the visual results show the superiority of the proposed technique over the conventional and state-of-art image super resolution techniques. For Lena's image, the PSNR is 6.52 dB higher than the bicubic interpolation.

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Toeplitz-based iterative image fusion scheme for MRI

Cited by 18 publications

References 6 publications

Super-resolution: a comprehensive survey

Super-resolution: a comprehensive survey

Reducible dictionaries for single image super-resolution based on patch matching and mean shifting

Image resolution enhancement by using interpolation followed by iterative back projection

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