mmediting/configs/restorers/liif

• LIIF (CVPR'2021)
• • Abstract
  • Results and models
  • Citation

LIIF (CVPR'2021)

Learning Continuous Image Representation with Local Implicit Image Function

Abstract

How to represent an image? While the visual world is presented in a continuous manner, machines store and see the images in a discrete way with 2D arrays of pixels. In this paper, we seek to learn a continuous representation for images. Inspired by the recent progress in 3D reconstruction with implicit neural representation, we propose Local Implicit Image Function (LIIF), which takes an image coordinate and the 2D deep features around the coordinate as inputs, predicts the RGB value at a given coordinate as an output. Since the coordinates are continuous, LIIF can be presented in arbitrary resolution. To generate the continuous representation for images, we train an encoder with LIIF representation via a self-supervised task with super-resolution. The learned continuous representation can be presented in arbitrary resolution even extrapolate to x30 higher resolution, where the training tasks are not provided. We further show that LIIF representation builds a bridge between discrete and continuous representation in 2D, it naturally supports the learning tasks with size-varied image ground-truths and significantly outperforms the method with resizing the ground-truths.

Results and models

Method	scale	Set5 PSNR / SSIM	Set14 PSNR / SSIM	DIV2K PSNR / SSIM	Download
liif_edsr_norm_c64b16_g1_1000k_div2k	x2	35.7131 / 0.9366	31.5579 / 0.8889	34.6647 / 0.9355	model | log
△	x3	32.3805 / 0.8915	28.4605 / 0.8039	30.9808 / 0.8724	△
△	x4	30.2748 / 0.8509	26.8415 / 0.7381	29.0245 / 0.8187	△
△	x6	27.1187 / 0.7774	24.7461 / 0.6444	26.7770 / 0.7425	△
△	x18	20.8516 / 0.5406	20.0096 / 0.4525	22.1987 / 0.5955	△
△	x30	18.8467 / 0.5010	18.1321 / 0.3963	20.5050 / 0.5577	△
liif_rdn_norm_c64b16_g1_1000k_div2k	x2	35.7874 / 0.9366	31.6866 / 0.8896	34.7548 / 0.9356	model | log
△	x3	32.4992 / 0.8923	28.4905 / 0.8037	31.0744 / 0.8731	△
△	x4	30.3835 / 0.8513	26.8734 / 0.7373	29.1101 / 0.8197	△
△	x6	27.1914 / 0.7751	24.7824 / 0.6434	26.8693 / 0.7437	△
△	x18	20.8913 / 0.5329	20.1077 / 0.4537	22.2972 / 0.5950	△
△	x30	18.9354 / 0.4864	18.1448 / 0.3942	20.5663 / 0.5560	△

Note:

• △ refers to ditto.
• Evaluated on RGB channels, scale pixels in each border are cropped before evaluation.

Citation

@inproceedings{chen2021learning,
  title={Learning continuous image representation with local implicit image function},
  author={Chen, Yinbo and Liu, Sifei and Wang, Xiaolong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={8628--8638},
  year={2021}
}