Weighted Adaptive Image Super-Resolution Scheme Based On Local Fractal Feature And Image Roughness

Image super-resolution aims to reconstruct a high-resolution image from the known low-resolution version. During this process, it should keep the degree of image roughness non-decreasing, which reflects various texture features and appearance. However, this point is not well addressed in the current work. This work argues that reducing roughness during image super-resolution is the key reason causing various problems such as artificial texture and/or edge blur. In this work, keeping the image roughness non-decreasing during super-resolution is being well investigated for the first time to our best knowledge. Image super-resolution is cast as an optimization problem to keep image roughness non-decreasing. In order to tackle this problem, the image super-resolution is approached based on the theory of fractal, where adaptive fractal interpolation function is proposed. In this way, the rational fractal interpolation model is adaptive to every local region. Thus, the roughness of every image region can be best maintained while super-resolution is carried out through fractal interpolation. In this work, the image roughness is reflected by the fractal dimension, which is a key element affecting the construction of fractal interpolation model. That is, the image roughness is measurable using fractal dimension. Mathematically, the overall image super-resolution process can be converted into a fractal interpolation optimization problem where the local fractal dimension is maintained. Although adaptive super-resolution on image segments may best maintain image roughness using the proposed method, it still generates unnecessary block artifacts. To tackle this problem, this work proposes a fine-grained pixel-wise fractal function. Our extensive experimental results demonstrate that the proposed method achieves encouraging performance with the state-of-the-art super-resolution algorithms.