A Novel Wavelet Based Super Resolution Reconstruction of Low Resolution Images Using Adaptive Interpolation

Syed, Liyakathunisa; Kumar, C. N. Ravi

doi:10.1109/artcom.2010.58

Cited by 8 publications

(7 citation statements)

References 12 publications

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“…This 2D wavelet decomposition will make four decomposed subband images referred to LL, LH, HL, and HH. All those four subbands cover the full frequency band of the original image [10][11][12]. Figure 3 shows the 3-level wavelet decomposed Barbara image.…”

Section: D Discrete Wavelet Transformmentioning

confidence: 99%

Wavelet Transform Based Image Interpolation Using PBP Cubic and Hybrid Filter

Han

2016

IJMUE

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In this paper, a new wavelet based image interpolation algorithm using pixel-by-pixel (PBP) cubic image interpolation and hybrid filter is proposed for images which include many mid and high frequency elements. There is much important information in frequency bands. Because conventional interpolation techniques have blurring or blocking effects, the quality of interpolated remote sensing images is not good over that of standard test images. The proposed interpolation technique uses PBP, DWT and hybrid filter to address these problems preserving thin lines and edges information. Simulation result shows that the proposed technique outperforms the conventional techniques in terms of PSNR.

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Section: D Discrete Wavelet Transformmentioning

confidence: 99%

Wavelet Transform Based Image Interpolation Using PBP Cubic and Hybrid Filter

Han

2016

IJMUE

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“…The LR input images are the result of projection of a high resolution image onto the image plane, followed by sampling. In general, the SR image methods are categorized into four main divisions: i) frequency domain based approach [4,5] , ii) interpolation-based approach [6,7] , iii) regularization-based approach [8][9][10] , and iv) learning based approach [11,12] . The first three categories get a higher-resolution image from a set of LR input images, while the last one achieves the same objective by exploiting the information provided by an image database.…”

Section: Sr Image Reconstructionmentioning

confidence: 99%

Study of optical microscanning reconstruction

Zhao

2012

Image Reconstruction From Incomplete Data VII

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Infrared images provide valuable information for many applications. However, compared to a visible image, the image quality is poor and its spatial resolution is limited due to the focal plane arrays cannot be made dense enough to yield a sufficiently high spatial sampling frequency, which consequently leads to image blurring. Optical micro-scanning technique has been proven to be an effective method to increase the resolution of images. This technique is able to produce high resolution (HR) images from a set of optically shifted images of low-resolution (LR). Over the last decade, optical micro-scanning technique has become one of the active topics of research, among this, the super-resolution (SR) reconstruction algorithms are the focus. This paper starts with the basic principle of SR reconstruction. Then several methods of high-precision movement registration algorithm and SR reconstruction algorithms were introduced. This study particularly focuses on the more recent development in motion estimation methods. Furthermore, an algorithm based on sub-pixel image registration that estimates the displacements of the LR image is presented. The critical steps in image registration are collecting feature points and estimating a spatial transformation especially when outliers are present. In this paper, the Harris corner detector is used to find the feature points and then the point feature is described by the neighborhood difference in order to reduce the sensitivity to illumination variations. Moreover, the Random Sample Consensus(RANSAC) algorithm is employed to build a transformation model. Simulation results demonstrate that the method can estimate the displacements accurately.

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“…To find both the PSF h and the true image f that maximize the following posterior probability (39) where is the likelihood of observations is the image prior, and is the PSF prior. In optimization probability is dropped as it is constant with respect to f & h. If parameterization is used for the prior distributions and introduced parameters are considered as random variables called hyperparameters than the posterior probability becomes; unknown image could be obtained as; (41) Instead of determining the optimal f and h simultaneously alternating minimization is used which alternatively updates the one variable while keeping the other fixed [23,24]. Equations for next estimate of f & h are as follows; (42) (43) and the corresponding alternating minimization iterations are Initial true image estimation obtained by applying a shock filter [25] or a bilateral filter [26] results in highly successful blind deconvolution algorithms with sharp edges in image.…”

Section: Alternating Minimizationmentioning

confidence: 99%

Image Restoration for Shift Invariant Blurs-A Survey

Shah¹,

Dalal²

2013

IJCA

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Even though Image Restoration being a conventional problem in image processing, is still a demanding area and has always attracted the interest of research community. Image restoration algorithms are important as it serves a wide range of real world applications such as astronomy, medical imaging and photo editing are just a few which demands a good quality image for further high level processing. Image restoration methods aim to reduce the degradations that have occurred while the digital image was being obtained. All natural images have gone through some sort of degradation when they are acquired, processed or displayed because of sensor noise, camera misfocus, blur caused by relative motion between object & camera, atmospheric turbulence and others. The paper deal with restoration of images degraded by linear space-invariant blurs. Paper presents mathematical modeling of linear shift-invariant image formation process, possible sources of degradation, and reviews some fundamental & specific methods of restoration. General TermsAlgorithms, Modeling, Restoration.

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A Novel Wavelet Based Super Resolution Reconstruction of Low Resolution Images Using Adaptive Interpolation

Cited by 8 publications

References 12 publications

Wavelet Transform Based Image Interpolation Using PBP Cubic and Hybrid Filter

Wavelet Transform Based Image Interpolation Using PBP Cubic and Hybrid Filter

Study of optical microscanning reconstruction

Image Restoration for Shift Invariant Blurs-A Survey

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