Computer
Adaptive interpolation of images
Signal Processing
Image interpolation using interpolative classified vector quantization
Image and Vision Computing
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
Hi-index | 12.05 |
This study presents a new adaptive scheme for developing kernel-based interpolation methods that simultaneously enhance spatial image resolution and preserve locally detailed edges. A new edge-adapted distance is first estimated according to local gradients information by combining fuzzy theory with genetic learning algorithm. This estimated distance is then employed in place of the original Euclidean distance in various interpolation methods. Additionally, a learning procedure based on genetic algorithm is presented to obtain crucial parameters of the fuzzy system automatically. Experimental results presented in numerical comparisons and in visual observations verify the effectiveness of the proposed adaptive framework for kernel-based interpolation methods.