Recursive high-resolution reconstruction of blurred multiframe images

Kim, S.P.; Su, Wei

doi:10.1109/icassp.1991.151028

Cited by 42 publications

(52 citation statements)

References 5 publications

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“…This problem has been previously treated in the literature [18][19][20][21][22][23][24][25][26][27][28][29]. The first treatment to the supper resolution reconstruction problem was an iterative frequency domain treatment since the Fourier transform has superior properties for translational shifts between observations [18][19][20]. The maximum a posteriori (MAP) estimation algorithm has been implemented in the field of image super resolution [21][22][23].…”

Section: Super-resolution Reconstruction Of Imagesmentioning

confidence: 99%

“…These algorithms aim at reducing the computational cost of the matrix inversion processes involved in the solution by using successive approximation methods for the estimation of the HR image. Most of the previously suggested solutions to this problem are based on the regularization theory [18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: Super-resolution Reconstruction Of Imagesmentioning

confidence: 99%

“…Image interpolation is the process by which a single HR image can be obtained from a single degraded LR one [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17], while super-resolution reconstruction of images aims at obtaining a single HR image either from several degraded still images or from several degraded multiframes [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. This paper introduces a group of image interpolation and image super-resolution algorithms and some comparisons between them.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

New Techniques to Conquer the Image Resolution Enhancement Problem

El‐Khamy¹,

Hadhoud²,

Dessouky³

et al. 2008

PIER B

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Abstract-This paper presents some new techniques for high resolution (HR) image processing and compares between them. The paper focuses on two main topics, image interpolation and image superresolution. By image interpolation, we mean extracting an HR image from a single Degraded low resolution (LR) image. Polynomial based image interpolation is reviewed. Some new techniques for adaptive image interpolation and inverse image interpolation are presented. The other topic treated in this paper is image super-resolution. By image super resolution, we mean extracting a single HR image either from multiple observations or multiple frames. The paper focuses on the problem of image super resolution using wavelet fusion and presents several super resolution reconstruction algorithms based on the idea of wavelet fusion.

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Section: Super-resolution Reconstruction Of Imagesmentioning

confidence: 99%

Section: Super-resolution Reconstruction Of Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

New Techniques to Conquer the Image Resolution Enhancement Problem

El‐Khamy¹,

Hadhoud²,

Dessouky³

et al. 2008

PIER B

View full text Add to dashboard Cite

show abstract

“…[7]This process is used in digital image processing which helps in removal of noise. It is based on the standard that signals with extreme and perhaps spurious detail has high variation.…”

Section: Total Variation De-noising (Tvd)mentioning

confidence: 99%

Image De-blurring and Supper-Resolution by Adaptive Sparse Domain Selection and Regularization

Banafar¹,

Kourav²

2016

IJCA

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The method of total variation is used as a significant and competent image prior model in the regularization based area of image processing. However, as the model showing total variation supports a piecewise steady explanation, this process is classify below high intensity noise in the level areas of the picture is often poor, and a small number of pseudo edges are produced. Under this work we build up a spatially adaptive total variation model. Initially, we extract the spatial data based on each and every pixel, then two filtering process are combined to control the collision of pseudo edges. It also includes, the spatial information weight is build and classified with k-means clustering, and the cluster controls the center value of regularization strength in every region. The tentative results, of both simulated as well as genuine datasets, exhibit that the projected methodology can effectively diminish the pseudo edges formed by the total variation regularization in the flat areas, and maintain the limited smoothness of the HR images. The proposed region based spatial information adaptive variation model can effectively reduce the cause of noise on the spatial data extraction and maintain strength with changes in the noise intensity in the SR process as compare to traditional pixel based spatial information adaptive methodology.

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“…They can predict or find the unknown high frequency components and then to obtain high-resolution enlarged images. Most of the studies done in the resolution enhancement are from a group of low-resolution image frames to extract one high-resolution image, and they are called multi-image resolution enhancement (MIRE) methods [2]- [5]. MIRE methods often require a very heavy computational load.…”

Section: Introductionmentioning

confidence: 99%

Arbitrary Scale Image Enlargement with the Prediction of High Frequency Components

Yuan

Taguchit

Kawamata

2005 IEEE International Symposium on Circuits and Systems

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Abstract-In this paper, we propose an arbitrary scale enlargement method of digital images based on the Laplacian pyramid (LP) representation. Through the calculation of the arbitrary scale LP stages we obtain the ability of arbitrary scale enlargement, which solves the problem that the conventional LP enlargement methods are only capable of expanding an image up by a factor of two in size. Experimental results show that the proposed method can work effectively on arbitrary scale image enlargement.

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Recursive high-resolution reconstruction of blurred multiframe images

Cited by 42 publications

References 5 publications

New Techniques to Conquer the Image Resolution Enhancement Problem

New Techniques to Conquer the Image Resolution Enhancement Problem

Image De-blurring and Supper-Resolution by Adaptive Sparse Domain Selection and Regularization

Arbitrary Scale Image Enlargement with the Prediction of High Frequency Components

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