Adaptive Interpolation Scheme for Image Magnification Based on Local Fractal Analysis

Abstract

In this paper, we constructed a new rational fractal interpolation model with scale factors and shape parameters. The model obtains different expressions by changing the scale factor, according with the diversity of image features. On the basis of the constructed model, this paper presents a novel adaptive rational fractal magnification (ARFM) algorithm based on local fractal feature analysis. Firstly, the image is divided into different regions according to an adaptive threshold determined by a calculated fractal dimension, and the scaling factor is calculated based on the relationship between global fractal dimension (GFD) and local fractal dimension (LFD). Secondly, for different regions, different interpolation forms are selected according to regional characteristics. Thirdly, parameter optimization and sub-block selection are studied to further enhance the quality of the magnified image. The experimental results illustrate that the performance of the proposed ARFM algorithm is very competitive compared with the latest magnification algorithms. INDEX TERMS Rational fractal model, local fractal analysis, ARFM algorithm, local adaptive threshold, sub-block selection.

The Novel Bilateral Quadratic Interpolation Image Super-resolution Algorithm

“…To preserve texture details of the image, Song et al use contours to divide the image into texture area and smooth area [8]. Each area uses different interpolation models to construct its amplifying area [9]. Thus, a multi-scale detail enhanced image is constructed.…”