Image super-resolution based on half quadratic splitting

Model-based optimization methods have been widely used in varies image restoration solutions and achieved some remarkable results. However, finding out a closed mathematical solution for certain priors remains a great challenge. To resolve this problem, this paper presents an improved model-based algorithm for single image super-resolution. Instead of focusing on specific prior knowledge, we exploit the optimization scheme of general image restoration formula. In our approach, the general format of model-optimization problem is transformed into an alternant renewal process through half quadratic splitting. This transform can also separate the optimization into a modular structure and allows us to optimize the fidelity term and regularization term separately. Of which the regular optimization process can be considered as a denoising process. Then the guided filter is taken as a denoiser to realize this optimization, which uses local linear transform to keep the detail and L2 norm constraint to smooth the noise of input image. Experiments with benchmark datasets and our own infrared images show that our method can surpass several famous model-based and data-based methods in PSNR and SSIM.