Frequency-Based Enhancement Network for Efficient Super-Resolution
Behjati, Parichehr (Centre de Visió per Computador)
Rodríguez López, Pau (ServiceNow Research)
Fernández Tena, Carles (Oxolo GmbH)
Mehri, Armin (Centre de Visió per Computador)
Roca i Marvà, Francesc Xavier (Universitat Autònoma de Barcelona)
Ozawa, Seiichi (Kobe University)
Gonzàlez, Jordi (Universitat Autònoma de Barcelona)

Data:	2022
Resum:	Recently, deep convolutional neural networks (CNNs) have provided outstanding performance in single image super-resolution (SISR). Despite their remarkable performance, the lack of high-frequency information in the recovered images remains a core problem. Moreover, as the networks increase in depth and width, deep CNN-based SR methods are faced with the challenge of computational complexity in practice. A promising and under-explored solution is to adapt the amount of compute based on the different frequency bands of the input. To this end, we present a novel Frequency-based Enhancement Block (FEB) which explicitly enhances the information of high frequencies while forwarding low-frequencies to the output. In particular, this block efficiently decomposes features into low- and high-frequency and assigns more computation to high-frequency ones. Thus, it can help the network generate more discriminative representations by explicitly recovering finer details. Our FEB design is simple and generic and can be used as a direct replacement of commonly used SR blocks with no need to change network architectures. We experimentally show that when replacing SR blocks with FEB we consistently improve the reconstruction error, while reducing the number of parameters in the model. Moreover, we propose a lightweight SR model - Frequency-based Enhancement Network (FENet) - based on FEB that matches the performance of larger models. Extensive experiments demonstrate that our proposal performs favorably against the state-of-the-art SR algorithms in terms of visual quality, memory footprint, and inference time.
Ajuts:	Agencia Estatal de Investigación PID2020-120311RB-I00
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Deep learning ; Frequency-based methods ; Lightweight architectures ; Single image super-resolution
Publicat a:	IEEE Access, Vol. 10 (May 2022) , p. 57383-57397, ISSN 2169-3536

DOI: 10.1109/ACCESS.2022.3176441

15 p, 3.4 MB

El registre apareix a les col·leccions:
Articles > Articles de recerca
Articles > Articles publicats