Depth maps are used in a wide range of applications from 3D rendering to 2D image effects such as Bokeh.
Although deep learning has enabled a huge leap forward in image inpainting, current methods are often unable to synthesize realistic high-frequency details.
Blind super-resolution (SR) methods aim to generate a high quality high resolution image from a low resolution image containing unknown degradations.
Ranked #2 on Blind Super-Resolution on DIV2KRK - 4x upscaling
In this paper, we first propose a joint VFI-SR framework for up-scaling the spatio-temporal resolution of videos from 2K 30 fps to 4K 60 fps.
Joint learning of super-resolution (SR) and inverse tone-mapping (ITM) has been explored recently, to convert legacy low resolution (LR) standard dynamic range (SDR) videos to high resolution (HR) high dynamic range (HDR) videos for the growing need of UHD HDR TV/broadcasting applications.
Joint SR and ITM is an intricate task, where high frequency details must be restored for SR, jointly with the local contrast, for ITM.
In video super-resolution, the spatio-temporal coherence between, and among the frames must be exploited appropriately for accurate prediction of the high resolution frames.