Image interpolation using interpolative classified vector quantization
Image and Vision Computing
Fuzzy-adapted linear interpolation algorithm for image zooming
Signal Processing
Locally edge-adapted distance for image interpolation based on genetic fuzzy system
Expert Systems with Applications: An International Journal
Two-stage interpolation algorithm based on fuzzy logics and edges features for image zooming
EURASIP Journal on Advances in Signal Processing
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This article presents an improved version of an algorithm designed to perform image restoration via nonlinear interpolative vector quantization (NLIVQ). The improvement results from using lapped blocks during the decoding process. The algorithm is trained on original and diffraction-limited image pairs. The discrete cosine transform is again used in the codebook design process to control complexity. Simulation results are presented which demonstrate improvements over the nonlapped algorithm in both observed image quality and peak signal-to-noise ratio. In addition, the nonlinearity of the algorithm is shown to produce super-resolution in the restored images