Heewon Kim

TL;DR: This paper develops an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods, and proposes a new multi-scale deepsuper-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model.

TL;DR: Zhang et al. as discussed by the authors developed an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods by removing unnecessary modules in conventional residual networks.

TL;DR: This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results and gauges the state-of-the-art in single imagesuper-resolution.

TL;DR: A simple but effective deep neural network for video frame interpolation, which is end-to-end trainable and is free from a motion estimation network component, and achieves outstanding performance compared to the existing models with a component for optical flow computation.

TL;DR: This paper proposes an auto-encoder-based framework that enables joint learning of the downscaling network and the upsc scaling network to maximize the restoration performance and validates the model’s generalization capability by applying it to the task of image colorization.