A Texture Preserving Image Interpolation Algorithm Based on Rational Function

2018 ◽  
Vol 9 (2) ◽  
pp. 36-56
Author(s):  
Hongwei Du ◽  
Yunfeng Zhang ◽  
Fangxun Bao ◽  
Ping Wang ◽  
Caiming Zhang
Keyword(s):  
Rational Function ◽  
State Of The Art ◽  
Image Interpolation ◽  
Rational Interpolation ◽  
Image Texture ◽  
Interpolation Algorithm ◽  
Edge Structure ◽  
Smooth Region ◽  
Pixel Value ◽  
Image Details

In this article, a type of bivariate rational interpolation function is constructed for preserving image texture structure, which integrates polynomial functions with a rational function. On the basis of this model, an image interpolation algorithm for texture preserving is proposed. First, an isoline method is employed to detect the image texture, and then the image can be divided into texture regions and smooth regions adaptively. Second, the smooth region and the textured region are interpolated by the polynomial model and the rational model, respectively. Finally, in order to preserve image texture direction, an objective function based on the gradient is constructed, and the weight of the correlation point is calculated, and the pixel value of the interpolation point is determined by convolution. Experimental results show that the proposed algorithm achieves good competitive performance compared with the state-of-the-art interpolation algorithms, especially in preserving image details and edge structure.

scholarly journals Analysis of Medical Image Resizing Using Bicubic Interpolation Algorithm

2021 ◽  
Vol 14 (1) ◽  
pp. 20
Author(s):  
Bambang Krismono Triwijoyo ◽  
Ahmat Adil
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Nearest Neighbor ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Image Interpolation ◽  
Interpolation Algorithm ◽  
Image Size ◽  
Pixel Value ◽  
Bicubic Interpolation

Image interpolation is the most basic requirement for many image processing tasks such as medical image processing. Image interpolation is a technique used in resizing an image. To change the image size, each pixel in the new image must be remapped to a location in the old image to calculate the new pixel value. There are many algorithms available for determining the new pixel value, most of which involve some form of interpolation between the closest pixels in the old image. In this paper, we use the Bicubic interpolation algorithm to change the size of medical images from the Messidor dataset and then analyze it by measuring it using three parameters Mean Square Error (MSE), Root Mean Squared Error (RMSE), and Peak Signal-to-Noise Ratio (PSNR), and compare the results with Bilinear and Nearest-neighbor algorithms. The results showed that the Bicubic algorithm is better than Bilinear and Nearest-neighbor and the larger the image dimensions are resized, the higher the degree of similarity to the original image, but the level of computation complexity also increases.

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 Adaptive CSLBP compressed image hashing

2019 ◽  
Vol 9 (4) ◽  
pp. 2982
Author(s):  
Varsha Patil ◽  
Tanuja Sarode
Keyword(s):  
Hamming Distance ◽  
Color Space ◽  
Image Authentication ◽  
Image Texture ◽  
Weight Factor ◽  
Image Hashing ◽  
Discrimination Capability ◽  
Content Change ◽  
Smooth Region ◽  
Texture Extraction

Hashing is popular technique of image authentication to identify malicious attacks and it also allows appearance changes in an image in controlled way. Image hashing is quality summarization of images. Quality summarization implies extraction and representation of powerful low level features in compact form. Proposed adaptive CSLBP compressed hashing method uses modified CSLBP (Center Symmetric Local Binary Pattern) as a basic method for texture extraction and color weight factor derived from L*a*b* color space. Image hash is generated from image texture. Color weight factors are used adaptively in average and difference forms to enhance discrimination capability of hash. For smooth region, averaging of colours used while for non-smooth region, color differencing is used. Adaptive CSLBP histogram is a compressed form of CSLBP and its quality is improved by adaptive color weight factor. Experimental results are demonstrated with two benchmarks, normalized hamming distance and ROC characteristics. Proposed method successfully differentiate between content change and content persevering modifications for color images.

Rational Interpolation of the Exponential Function

1995 ◽  
Vol 47 (6) ◽  
pp. 1121-1147 ◽  
Author(s):  
L. Baratchart ◽  
E. B. Saff ◽  
F. Wielonsky
Keyword(s):  
Rational Function ◽  
Exponential Function ◽  
Complex Plane ◽  
Rational Interpolation ◽  
Real Interval ◽  
Compact Set ◽  
Rational Approximants ◽  
Sharp Estimates ◽  
Image Position ◽  
Uniformly Bounded

AbstractLet m, n be nonnegative integers and B(m+n) be a set of m + n + 1 real interpolation points (not necessarily distinct). Let Rm,n = Pm,n/Qm.n be the unique rational function with deg Pm,n ≤ m, deg Qm,n ≤ n, that interpolates ex in the points of B(m+n). If m = mv, n = nv with mv + nv → ∞, and mv / nv → λ as v → ∞, and the sets B(m+n) are uniformly bounded, we show that locally uniformly in the complex plane C, where the normalization Qm,n(0) = 1 has been imposed. Moreover, for any compact set K ⊂ C we obtain sharp estimates for the error |ez — Rm,n(z)| when z ∈ K. These results generalize properties of the classical Padé approximants. Our convergence theorems also apply to best (real) Lp rational approximants to ex on a finite real interval.

AN IMPROVED EDGE-DIRECTED IMAGE INTERPOLATION ALGORITHM

2012 ◽  
Vol 12 (04) ◽  
pp. 1250023 ◽  
Author(s):  
XINGYUAN WANG ◽  
ZHIFENG CHEN ◽  
XUEMEI BAO
Keyword(s):  
Real Time ◽  
Local Structure ◽  
Time Complexity ◽  
Image Interpolation ◽  
Threshold Value ◽  
Subjective Quality ◽  
Experimental Results ◽  
Interpolation Algorithm ◽  
Visual Effects ◽  
Structure Characteristics

The paper sets forth an improved edge-directed image interpolation algorithm with low time complexity. The algorithm partitions images into homogeneous and edge areas by setting the preset threshold value based on the local structure characteristics. Specified algorithms are assigned to interpolate each classified areas respectively. The proposed method implements strategy in three steps to interpolate after setting the preset threshold value. In this way, it can achieve the goals of real-time interpolation and good subjective quality. Furthermore, the interpolated images have much more explicit edge regions and better visual effects using our proposed method than that of using other algorithms. Experimental results demonstrate that the method proposed by the authors is high-performed in image interpolation.

scholarly journals Fully Convolutional Network with Multi-Step Reinforcement Learning for Image Processing

2019 ◽  
Vol 33 ◽  
pp. 3598-3605 ◽  
Author(s):  
Ryosuke Furuta ◽  
Naoto Inoue ◽  
Toshihiko Yamasaki
Keyword(s):  
Image Processing ◽  
Reinforcement Learning ◽  
State Of The Art ◽  
Great Success ◽  
Local Color ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Effective Learning ◽  
Pixel Value ◽  
Problem Setting

This paper tackles a new problem setting: reinforcement learning with pixel-wise rewards (pixelRL) for image processing. After the introduction of the deep Q-network, deep RL has been achieving great success. However, the applications of deep RL for image processing are still limited. Therefore, we extend deep RL to pixelRL for various image processing applications. In pixelRL, each pixel has an agent, and the agent changes the pixel value by taking an action. We also propose an effective learning method for pixelRL that significantly improves the performance by considering not only the future states of the own pixel but also those of the neighbor pixels. The proposed method can be applied to some image processing tasks that require pixel-wise manipulations, where deep RL has never been applied.We apply the proposed method to three image processing tasks: image denoising, image restoration, and local color enhancement. Our experimental results demonstrate that the proposed method achieves comparable or better performance, compared with the state-of-the-art methods based on supervised learning.

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