New Techniques to Conquer the Image Resolution Enhancement Problem

El‐Khamy, Said E.; Hadhoud, Mohiy M.; Dessouky, Moawad I.; Salam, B. M.; El‐Samie, Fathi E. Abd

doi:10.2528/pierb08020404

Cited by 9 publications

(17 citation statements)

References 31 publications

(129 reference statements)

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“…Some of the prior art focused on improving the accuracy of interpolated values by replacing the original distance δ with a warped distance δ′ measured by using local gradients (Ramponi,1999). Later, El‐Khamy et al (2005, 2008) suggested an approach in which accuracy of the image interpolation is improved using space variant adaptive weights and keeping distance δ fixed. The idea underlying this work is motivated by the work of El‐Khamy et al (2005, 2008) to produce an adaptive weight by using local gradients and multiplying the weights with the convolution kernel without changing the distance δ.…”

Section: Methodsmentioning

confidence: 99%

“…Later, El‐Khamy et al (2005, 2008) suggested an approach in which accuracy of the image interpolation is improved using space variant adaptive weights and keeping distance δ fixed. The idea underlying this work is motivated by the work of El‐Khamy et al (2005, 2008) to produce an adaptive weight by using local gradients and multiplying the weights with the convolution kernel without changing the distance δ. We extended it for 3D to enhance the depth resolution for confocal images.…”

Section: Methodsmentioning

confidence: 99%

“…However, those are also known to produce oscillatory edges because of applying a fixed kernel without considering local structural characteristics (Hussein and Eric,2005). To cope with image artifacts and edge blurring in edge regions, recently, a large number of edge‐based adaptive interpolation approaches have been proposed to improve perceptual quality of reproduced images by analyzing edge features (Carrato and Tenze,2000; Cha and Kim,2007; Chen and Wang,2010; Chen et al,2005; El‐Khamy et al,2005, 2008; Huang and Lee,2004; Huang and Zheng,2006; Li and Orchard,2001; Ramponi,1999; Shao and Wei,2008; Zhang and Wu,2006). Compared to simple linear interpolators, high‐order B‐spline kernel accomplishes more accurate results but with high computation time.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive‐weighted cubic B‐spline using lookup tables for fast and efficient axial resampling of 3D confocal microscopy images

Indhumathi

Cai

Guan

et al. 2012

Microscopy Res & Technique

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Confocal laser scanning microscopy has become a most powerful tool to visualize and analyze the dynamic behavior of cellular molecules. Photobleaching of fluorochromes is a major problem with confocal image acquisition that will lead to intensity attenuation. Photobleaching effect can be reduced by optimizing the collection efficiency of the confocal image by fast z-scanning. However, such images suffer from distortions, particularly in the z dimension, which causes disparities in the x, y, and z directions of the voxels with the original image stacks. As a result, reliable segmentation and feature extraction of these images may be difficult or even impossible. Image interpolation is especially needed for the correction of undersampling artifact in the axial plane of three-dimensional images generated by a confocal microscope to obtain cubic voxels. In this work, we present an adaptive cubic B-spline-based interpolation with the aid of lookup tables by deriving adaptive weights based on local gradients for the sampling nodes in the interpolation formulae. Thus, the proposed method enhances the axial resolution of confocal images by improving the accuracy of the interpolated value simultaneously with great reduction in computational cost. Numerical experimental results confirm the effectiveness of the proposed interpolation approach and demonstrate its superiority both in terms of accuracy and speed compared to other interpolation algorithms.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive‐weighted cubic B‐spline using lookup tables for fast and efficient axial resampling of 3D confocal microscopy images

Indhumathi

Cai

Guan

et al. 2012

Microscopy Res & Technique

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“…The resolution enhancement is an interpolation process of calculating the values of intermediate pixels between the known pixels of certain low resolution measurement [1]. Such kind of methods is commonly divided into different categories, such as bilinear interpolation, nearest neighbor interpolation, bicubic interpolation [2], and adaptive cubic spline [3].…”

Section: Introductionmentioning

confidence: 99%

Resolution Enhancement Method Used for Force Sensing Resistor Array

et al. 2015

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Tactile sensors are one of the major devices that enable robotic systems to interact with the surrounding environment. This research aims to propose a mathematical model to describe the behavior of a tactile sensor based on experimental and statistical analyses and moreover to develop a versatile algorithm that can be applied to different tactile sensor arrays to enhance the limited resolution. With the proposed algorithm, the resolution can be increased up to twenty times if multiple measurements are available. To verify if the proposed algorithm can be used for tactile sensor arrays that are used in robotic system, a 16 × 10 force sensing array (FSR) is adopted. The acquired two-dimensional measurements were processed by a resolution enhancement method (REM) to enhance the resolution, which can be used to improve the resolution for single image or multiple measurements. As a result, the resolution of the sensor is increased and it can be used as synthetic skin to identify accurate shapes of objects and applied forces.

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“…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. [1] Pingxiang Li, Huanfeng Shen, and Liangpei Zhang proposed that in the field of digital photogrammetry, it is very important to enhance the image resolution. By enhancement, a clearer image with higher resolution is produced.…”

Section: IImentioning

confidence: 99%