Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Adaptive interpolation of images
Signal Processing
Efficient implementation of image interpolation as an inverse problem
Digital Signal Processing
A fast edge-oriented algorithm for image interpolation
Image and Vision Computing
Warped distance for space-variant linear image interpolation
IEEE Transactions on Image Processing
Regularity-preserving image interpolation
IEEE Transactions on Image Processing
Image interpolation using neural networks
IEEE Transactions on Image Processing
Lapped nonlinear interpolative vector quantization and image super-resolution
IEEE Transactions on Image Processing
New edge-directed interpolation
IEEE Transactions on Image Processing
A note on cubic convolution interpolation
IEEE Transactions on Image Processing
Two-dimensional cubic convolution
IEEE Transactions on Image Processing
Adaptively quadratic (AQua) image interpolation
IEEE Transactions on Image Processing
Locally adaptive wavelet-based image interpolation
IEEE Transactions on Image Processing
Image interpolation by two-dimensional parametric cubic convolution
IEEE Transactions on Image Processing
An edge-guided image interpolation algorithm via directional filtering and data fusion
IEEE Transactions on Image Processing
A New Orientation-Adaptive Interpolation Method
IEEE Transactions on Image Processing
The Error-Amended Sharp Edge (EASE) Scheme for Image Zooming
IEEE Transactions on Image Processing
Image interpolation for progressive transmission by using radial basis function networks
IEEE Transactions on Neural Networks
A fully automatic one-scan adaptive zooming algorithm for color images
Signal Processing
Fuzzy spline interpolation with optimal property in parametric form
Information Sciences: an International Journal
Simultaneous image interpolation for stereo images
Signal Processing
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This work presents a novel fuzzy linear interpolation algorithm with application in image zooming. Fuzzy logics are employed to derive suitable weights for the neighboring samples in the interpolation formulae. By considering local gradients to calculate the weights, the accuracy of the interpolated value is improved. Additionally, a modification of the proposed algorithm based on the interpolation error theorem is developed to deal with images containing ridges and valleys. Both quantitative results obtained by measuring the peak signal-to-noise ratio (PSNR) and perceptual observations assessed the superior performance of the proposed algorithm and its modified version with respect to the state-of-the-art interpolation methods.