High Quality Image Interpolation via Local Autoregressive and Nonlocal 3-D Sparse Regularization

TL;DR

This paper introduces a new image interpolation method combining local autoregressive and nonlocal 3-D sparse models, optimized with a Split-Bregman algorithm, resulting in superior image quality.

Contribution

The paper presents a novel interpolation algorithm that integrates local AR and nonlocal sparse models with a new optimization approach, improving over traditional methods.

Findings

Significant improvement in image quality over traditional algorithms

Enhanced visual perception of interpolated images

Robustness in numerical stability during optimization

Abstract

In this paper, we propose a novel image interpolation algorithm, which is formulated via combining both the local autoregressive (AR) model and the nonlocal adaptive 3-D sparse model as regularized constraints under the regularization framework. Estimating the high-resolution image by the local AR regularization is different from these conventional AR models, which weighted calculates the interpolation coefficients without considering the rough structural similarity between the low-resolution (LR) and high-resolution (HR) images. Then the nonlocal adaptive 3-D sparse model is formulated to regularize the interpolated HR image, which provides a way to modify these pixels with the problem of numerical stability caused by AR model. In addition, a new Split-Bregman based iterative algorithm is developed to solve the above optimization problem iteratively. Experiment results demonstrate that the proposed algorithm achieves significant performance improvements over the traditional algorithms in terms of both objective quality and visual perception