Proceedings of world academy of science, engineering and technology, vol 8

Image interpolation is the problem of increasing the resolution of a given image. An important aspect of interpolation is accurate estimation of edge orientations. This work introduces contour stencils, a new method for estimating the image contours based on total variation along curves. Contour stencils are able to distinguish lines of different orientations, curves, corners, and other geometric features with a computationally efficient formula. Contour stencils are applied in designing an edge directed color interpolation method. The method incorporates an efficient approximation of deconvolution. Although most interpolation methods that involve deconvolution require either solving a large linear system or running many iterations, this method has linear complexity in the number of pixels and can be computed in one or a small number of passes through the image. Comparisons show that the proposed interpolation is competitive with existing methods.

Contour Stencils: Total Variation along Curves for Adaptive Image Interpolation

Interpolation has become a default operation in image processing and medical imaging and is one of the important factors in the success of an intensity-based registration method. Interpolation is needed if the fractional unit ofmotion is not matched and located on the high resolution (HR) grid. The purpose of this work is to present a systematic evaluation of eight standard interpolation techniques (trilinear, nearest neighbor, cubic Lagrangian, quintic Lagrangian, hepatic Lagrangian, windowed Sinc, B-spline 3rd order, and B-spline 4th order) and to compare the effect of cost functions (least squares (LS), normalized mutual information (NMI), normalized cross correlation (NCC), and correlation ratio (CR)) for optimized automatic image registration (OAIR) on 3D spoiled gradient recalled (SPGR) magnetic resonance images (MRI) of the brain acquired using a 3T GE MR scanner. Subsampling was performed in the axial, sagittal, and coronal directions to emulate three low resolution datasets. Afterwards, the low resolution datasets were upsampled using different interpolation methods, and they were then compared to the high resolution data. Themean squared error, peak signal to noise, joint entropy, and cost functions were computed for quantitative assessment of the method. Magnetic resonance image scans and joint histogram were used for qualitative assessment of the method.

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Evaluation of interpolation effects on upsampling and accuracy of cost functions-based optimized automatic image registration

Standard clinical magnetic resonance imaging (MRI) is acquired in two-dimensions where the in-plane resolution is higher than the slice select direction. These acquisitions include axial, coronal, and sagittal planes. To date, there have been few attempts to combine the information of these three orthogonal orientations. This paper aims to take advantage of the different in-plane resolution acquired from each plane orientation and combine them into one volume in order to attain a higher resolution image. This combination of MRI data will allow the detection of smaller areas that would otherwise be missed using only one slice orientation. A comparison of slice thicknesses along with image registration is performed. The mean-squared error and peak signal-to-noise were computed for quantitative assessment. MRI and phantom scans and joint histograms were used for qualitative assessment.

/pdf/interpolation-based-super-resolution-reconstruction-effects-rev0kupum2.pdf

Interpolation-Based Super-Resolution Reconstruction: Effects of Slice Thickness

In this paper, a super-resolution technique is proposed that uses a combination of bicubic interpolation and wavelet transform. Bicubic interpolation produces a high resolution image but is prone to blurring artifact. So the blurring artifact is reduced in the wavelet domain. The input low-resolution is up-sampled using bicubic interpolation. The edges of the resultant high-resolution image are enhanced using stationary wavelet transform (SWT). SWT is applied to the image to produce sub-bands of the image and then these sub-bands are modified by multiplying with a boost value. Then these sub-bands are combined using inverse stationary wavelet transform (ISWT) to produce the final high-resolution image. The quantitative and qualitative analysis illustrate that the proposed technique is provides superior results as compared to other existing techniques.

Super-Resolution Using Edge Modification through Stationary Wavelet Transform

World has entered in 21 century. The technology of computer graphics and digital cameras is prevalent. High resolution display and printer are available. Therefore high resolution images are needed in order to produce high quality display images and high quality prints. However, since high resolution images are not usually provided, there is a need to magnify the original images. One common difficulty in the previous magnification techniques is that of preserving details, i.e. edges and at the same time smoothing the data for not introducing the spurious artefacts. A definitive solution to this is still an open issue. In this paper an image magnification using adaptive interpolation by pixel level data-dependent geometrical shapes is proposed that tries to take into account information about the edges (sharp luminance variations) and smoothness of the image. It calculate threshold, classify interpolation region in the form of geometrical shapes and then assign suitable values inside interpolation region to the undefined pixels while preserving the sharp luminance variations and smoothness at the same time. The results of proposed technique has been compared qualitatively and quantitatively with five other techniques. In which the qualitative results show that the proposed method beats completely the Nearest Neighbouring (NN), bilinear(BL) and bicubic(BC) interpolation. The quantitative results are competitive and consistent with NN, BL, BC and others. Keywords—Adaptive, digital image processing, image magnification, interpolation, geometrical shapes, qualitative & quantitative analysis.

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Noman Jafri

Papers

Image Magnification Using Adaptive Interpolationby Pixel Level Data-Dependent Geometrical Shapes