Antialiased super-resolution with parallel high-frequency synthesis

Image super-resolution (SR) increases the resolution of the target image, and has become a fundamental image-editing operation for real-world applications. Traditional methods often cause jaggies and blurring artifacts because natural images generally contain a lot of discrete continuities and edges. This paper proposes a new synthesis-based method for image super-resolution at a pixel level that takes advantages of convolution-based edge anti-aliasing. The target images are divided into two components representing, respectively, the high- and low-frequency contents of the images. We perform bicubic interpolation to reconstruct the missing information in the low-frequency component. A patch-based texture synthesis is subsequently adopted to synthesize the high-frequency patches with the final upscaled images. In particular, we also use the efficient edge-based anti-aliasing for correcting the quantization error, restore the high-frequency details damaged by nonlinear example-based synthesis. Our proposed approach generates super-resolution images dynamically and can be fully implemented in GPU parallelization. Experiments confirm the visual superiority of our proposed approach in comparison with competing state-of-the-art techniques.