An Image Interpolation Method Based on Weighted Subdivision

2017 ◽  
Vol 32 (04) ◽  
pp. 1854009 ◽  
Author(s):  
Cheng-ming Liu ◽  
Hai-bo Pang ◽  
Liang-pin Ren ◽  
Zhe Zhao ◽  
Shu-yan Zhang
Keyword(s):  
High Resolution ◽  
Interpolation Method ◽  
Image Interpolation ◽  
Subdivision Scheme ◽  
Interpolation Algorithm ◽  
High Resolution Image ◽  
Sharp Edges ◽  
Image Edges ◽  
Weight Coefficients ◽  
Smooth Data

In this paper, we propose a new image interpolation algorithm by using geometric subdivision. Similar to image upsampling, the geometric subdivision can supplement unknown data according to a certain rule, but it can only generate smooth data. To preserve the sharp edges of the high-resolution image, we adopt a rational subdivision scheme. By adjusting the weight coefficients of the rational subdivision, we can control the mesh shape near sharp edges. Hence, the image edges are preserved.

Based on the Edge of the Threshold Control Adaptive Fast Image Interpolation Algorithm and its Improvement

2014 ◽  
Vol 926-930 ◽  
pp. 3000-3003
Author(s):  
Xiao Ju Ma ◽  
Lin Yun Zhou ◽  
Yu Gao
Keyword(s):  
High Resolution ◽  
Image Interpolation ◽  
Control Mode ◽  
Image Texture ◽  
Interpolation Algorithm ◽  
Low Resolution ◽  
Threshold Control ◽  
Resolution Image ◽  
High Resolution Image ◽  
Low Resolution Images

This paper presents an improvement fast image interpolation algorithm, which we divided the low resolution images into smooth area, edge area and texture area based on threshold control mode, then we using three channel to achieve fast interpolation. Experiments show that this method makes the image texture details clear, won the high resolution image.

scholarly journals Image Interpolation via Gradient Correlation-Based Edge Direction Estimation

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Sajid Khan ◽  
Dong-Ho Lee ◽  
Muhammad Asif Khan ◽  
Muhammad Faisal Siddiqui ◽  
Raja Fawad Zafar ◽  
...  
Keyword(s):  
High Resolution ◽  
Interpolation Method ◽  
Linear Method ◽  
Image Interpolation ◽  
Input Image ◽  
Edge Direction ◽  
Intelligent Decision ◽  
Gradient Based ◽  
The Cost ◽  
Hysteresis Thresholding

This paper introduces an image interpolation method that provides performance superior to that of the state-of-the-art algorithms. The simple linear method, if used for interpolation, provides interpolation at the cost of blurring, jagging, and other artifacts; however, applying complex methods provides better interpolation results, but sometimes they fail to preserve some specific edge patterns or results in oversmoothing of the edges due to postprocessing of the initial interpolation process. The proposed method uses a new gradient-based approach that makes an intelligent decision based on the edge direction using the edge map and gradient map of an image and interpolates unknown pixels in the predicted direction using known intensity pixels. The input image is subjected to the efficient hysteresis thresholding-based edge map calculation, followed by interpolation of low-resolution edge map to obtain a high-resolution edge map. Edge map interpolation is followed by classification of unknown pixels into obvious edges, uniform regions, and transitional edges using the decision support system. Coefficient-based interpolation that involves gradient coefficient and distance coefficient is applied to obvious edge pixels in the high-resolution image, whereas transitional edges in the neighborhood of an obvious edge are interpolated in the same direction to provide uniform interpolation. Simple line averaging is applied to pixels that are not detected as an edge to decrease the complexity of the proposed method. Applying line averaging to smooth pixels helps to control the complexity of the algorithm, whereas applying gradient-based interpolation preserves edges and hence results in better performance at reasonable complexity.

scholarly journals Energy-Driven Image Interpolation Using Gaussian Process Regression

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Lingling Zi ◽  
Junping Du
Keyword(s):  
Gaussian Process ◽  
Information Sources ◽  
Classical Problem ◽  
Gaussian Process Regression ◽  
Image Interpolation ◽  
Interpolation Algorithm ◽  
Resolution Image ◽  
High Resolution Image ◽  
Computation Technique ◽  
Energy Computation

Image interpolation, as a method of obtaining a high-resolution image from the corresponding low-resolution image, is a classical problem in image processing. In this paper, we propose a novel energy-driven interpolation algorithm employing Gaussian process regression. In our algorithm, each interpolated pixel is predicted by a combination of two information sources: first is a statistical model adopted to mine underlying information, and second is an energy computation technique used to acquire information on pixel properties. We further demonstrate that our algorithm can not only achieve image interpolation, but also reduce noise in the original image. Our experiments show that the proposed algorithm can achieve encouraging performance in terms of image visualization and quantitative measures.

scholarly journals Single Image Super-Resolution viaL0Image Smoothing

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhang Liu ◽  
Qi Huang ◽  
Jian Li ◽  
Qi Wang
Keyword(s):  
High Resolution ◽  
Super Resolution ◽  
Smoothing Method ◽  
Single Image ◽  
Low Resolution ◽  
Resolution Image ◽  
High Resolution Image ◽  
Sharp Edges ◽  
Image Super Resolution ◽  
Single Image Super Resolution

We propose a single image super-resolution method based on aL0smoothing approach. We consider a low-resolution image as two parts: one is the smooth image generated by theL0smoothing method and the other is the error image between the low-resolution image and the smoothing image. We get an intermediate high-resolution image via a classical interpolation and then generate a high-resolution smoothing image with sharp edges by theL0smoothing method. For the error image, a learning-based super-resolution approach, keeping image details well, is employed to obtain a high-resolution error image. The resulting high-resolution image is the sum of the high-resolution smoothing image and the high-resolution error image. Experimental results show the effectiveness of the proposed method.

Radiation Damage, Contrast and Statistics in High Resolution Biological Electron Microscopy

1970 ◽  
Vol 28 ◽  
pp. 260-261
Author(s):  
Robert M. Glaeser
Keyword(s):  
High Resolution ◽  
Particle Flux ◽  
Unit Area ◽  
Signal To Noise ◽  
Resolution Image ◽  
High Resolution Image ◽  
Pass Through ◽  
Counting Statistics ◽  
The Relationship ◽  
Picture Element

It is well known that a large flux of electrons must pass through a specimen in order to obtain a high resolution image while a smaller particle flux is satisfactory for a low resolution image. The minimum particle flux that is required depends upon the contrast in the image and the signal-to-noise (S/N) ratio at which the data are considered acceptable. For a given S/N associated with statistical fluxtuations, the relationship between contrast and “counting statistics” is s131_eqn1, where C = contrast; r2 is the area of a picture element corresponding to the resolution, r; N is the number of electrons incident per unit area of the specimen; f is the fraction of electrons that contribute to formation of the image, relative to the total number of electrons incident upon the object.

Effect of strain on ADF-STEM high-resolution images

1991 ◽  
Vol 49 ◽  
pp. 666-667
Author(s):  
M. Kelly ◽  
D.M. Bird
Keyword(s):  
High Resolution ◽  
Strong Influence ◽  
Intensity Variation ◽  
Dark Field ◽  
Atomic Resolution ◽  
Strain Fields ◽  
High Resolution Image ◽  
Annular Dark Field ◽  
High Resolution Images ◽  
Interesting Alternative

It is well known that strain fields can have a strong influence on the details of HREM images. This, for example, can cause problems in the analysis of edge-on interfaces between lattice mismatched materials. An interesting alternative to conventional HREM imaging has recently been advanced by Pennycook and co-workers where the intensity variation in the annular dark field (ADF) detector is monitored as a STEM probe is scanned across the specimen. It is believed that the observed atomic-resolution contrast is correlated with the intensity of the STEM probe at the atomic sites and the way in which this varies as the probe moves from cell to cell. As well as providing a directly interpretable high-resolution image, there are reasons for believing that ADF-STEM images may be less suseptible to strain than conventional HREM. This is because HREM images arise from the interference of several diffracted beams, each of which is governed by all the excited Bloch waves in the crystal.

Detection of a point defect in a silicon single crystal by high-resolution TEM

1995 ◽  
Vol 53 ◽  
pp. 466-467
Author(s):  
M. Awaji
Keyword(s):  
High Resolution ◽  
Single Crystal ◽  
Point Defect ◽  
Point Defects ◽  
Noise Level ◽  
Dark Field ◽  
Silicon Single Crystal ◽  
High Resolution Image ◽  
Dark Field Imaging ◽  
Field Imaging

It is necessary to improve the resolution, brightness and signal-to-noise ratio(s/n) for the detection and identification of point defects in crystals. In order to observe point defects, multi-beam dark-field imaging is one of the useful methods. Though this method can improve resolution and brightness compared with dark-field imaging by diffuse scattering, the problem of s/n still exists. In order to improve the exposure time due to the low intensity of the dark-field image and the low resolution, we discuss in this paper the bright-field high-resolution image and the corresponding subtracted image with reference to a changing noise level, and examine the possibility for in-situ observation, identification and detection of the movement of a point defect produced in the early stage of damage process by high energy electron bombardment.The high-resolution image contrast of a silicon single crystal in the [10] orientation containing a triple divacancy cluster is calculated using the Cowley-Moodie dynamical theory and for a changing gaussian noise level. This divacancy model was deduced from experimental results obtained by electron spin resonance. The calculation condition was for the lMeV Berkeley ARM operated at 800KeV.

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