Depth Image Super-Resolution Using Correlation-Controlled Color Guidance and Multi-Scale Symmetric Network

Tao Li,Hongwei Lin,Xiucheng Dong,Xiaohua Zhang

Pattern Recognition（2020）

Xihua Univ | Northwest Minzu Univ | Hiroshima Inst Technol

Cited 20|Views17

Abstract

•We develop an effective symmetric unit (SU) with the ability of residual learning to reconstruct edge details and restore edge sharpness.•We use chains of SUs to construct a multi-scale symmetric network architecture (MSSNet) with dense color guidance to progressively up-sample depth images.•A novel structure called correlation-controlled color guidance block (CCGB) is introduced by investigating the inter-channel correlation between depth inference network and color guidance network to improve the color guidance accuracy.•We integrate the MSSNet and the CCGB into a unified framework to effectively resolve the problem of depth image super resolution.

Translated text

Key words

Depth image super-resolution,Deep convolutional neural network,Encoder-decoder structure,Color guidance,Channel correlation

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper