Fast Image Restoration With Multi-Bin Trainable Linear Units

Shuhang Gu, Wen Li, Luc Van Gool, Radu Timofte; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 4190-4199

Abstract


Tremendous advances in image restoration tasks such as denoising and super-resolution have been achieved using neural networks. Such approaches generally employ very deep architectures, large number of parameters, large receptive fields and high nonlinear modeling capacity. In order to obtain efficient and fast image restoration networks one should improve upon the above mentioned requirements. In this paper we propose a novel activation function, the multi-bin trainable linear unit (MTLU), for increasing the nonlinear modeling capacity together with lighter and shallower networks. We validate the proposed fast image restoration networks for image denoising (FDnet) and super-resolution (FSRnet) on standard benchmarks. We achieve large improvements in both memory and runtime over current state-of-the-art for comparable or better PSNR accuracies.

Related Material


[pdf]
[bibtex]
@InProceedings{Gu_2019_ICCV,
author = {Gu, Shuhang and Li, Wen and Gool, Luc Van and Timofte, Radu},
title = {Fast Image Restoration With Multi-Bin Trainable Linear Units},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}