Edge-Informed Single Image Super-Resolution
This addresses the demand for higher-resolution images in digital imaging, offering an incremental improvement by decoupling structure and texture reconstruction.
The paper tackles single image super-resolution by reformulating it as an image inpainting task, using a two-stage model that jointly optimizes texture and edge structures, showing effectiveness for scale factors up to x8 compared to interpolation and state-of-the-art methods.
The recent increase in the extensive use of digital imaging technologies has brought with it a simultaneous demand for higher-resolution images. We develop a novel edge-informed approach to single image super-resolution (SISR). The SISR problem is reformulated as an image inpainting task. We use a two-stage inpainting model as a baseline for super-resolution and show its effectiveness for different scale factors (x2, x4, x8) compared to basic interpolation schemes. This model is trained using a joint optimization of image contents (texture and color) and structures (edges). Quantitative and qualitative comparisons are included and the proposed model is compared with current state-of-the-art techniques. We show that our method of decoupling structure and texture reconstruction improves the quality of the final reconstructed high-resolution image. Code and models available at: https://github.com/knazeri/edge-informed-sisr