Single image super-resolution under multi-frame method

The multi-frame image super-resolution method utilizes a series of low-resolution images from the same scene to reconstruct the corresponding high-quality super-resolution image. However, the insufficient input low-resolution images make it incapable of reconstructing the high-resolution image from a single low-resolution image. In this work, we extend the framework of multi-frame image super-resolution to handle the single low-resolution image via rolling guidance filtering. And an improved version of diffusion-driven regularizer-based multi-frame image super-resolution algorithm is proposed and applied on passive millimeter-wave (PMMW) image super-resolution. Specifically, the joint filtering is first exploited to suppress the noise of single low-resolution noisy image. The rolling guidance method is exploited to generate the structurally multi-scale low-resolution images forming the basis of multi-frame image super-resolution. The generated image sequences are then fed to the nonlinear diffusion regularizer-based super-resolution algorithm. The two-directional total variation de-convolution is finally employed to remove the blur, producing a sharp and clear high-resolution image. Experiments demonstrate the effectiveness of the proposed method and show its superiority for the natural and PMMW images.