BACK-PROJECTION PIPELINE

被引：2

作者：

Michelini, Pablo Navarrete ^{[1
]}

Liu, Hanwen ^{[1
]}

Lu, Yunhua ^{[1
]}

Jiang, Xingqun ^{[1
]}

机构：

[1] BOE Technol Co Ltd, Beijing, Peoples R China

来源：

2021 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2021年

关键词：

Super-resolution; back-projection; deep learning; multi-scale; causality; RENORMALIZATION-GROUP;

D O I：

10.1109/ICIP42928.2021.9506014

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We propose a simple extension of residual networks that works simultaneously in multiple resolutions for the problem of image super-resolution. Our network design is inspired by the iterative back-projection algorithm and seeks the more difficult task of learning how to enhance images. Compared to similar approaches, we propose a novel solution to make back-projections run in multiple resolutions by using a data pipeline workflow. Features are updated at multiple scales in each layer of the network. The update dynamic through these layers includes interactions between different resolutions in a way that is causal in scale, and it is represented by a system of ODEs, as opposed to a single ODE in the case of ResNets.

引用

页码：1949 / 1953

页数：5

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