Self-Supervised Learning for RGB-Guided Depth Enhancement by Exploiting the Dependency Between RGB and Depth

RGB颜色模型 人工智能 计算机科学 依赖关系(UML) 计算机视觉 降噪 噪音(视频) 模式识别(心理学) 图像(数学)
作者
Jun Wang,Peilin Liu,Fei Wen
出处
期刊:IEEE transactions on image processing [Institute of Electrical and Electronics Engineers]
卷期号:32: 159-174 被引量:5
标识
DOI:10.1109/tip.2022.3226419
摘要

Due to the imaging mechanism of time-of-flight (ToF) sensors, the captured depth images usually suffer from severe noise and degradation. Though many RGB-guided methods have been proposed for depth image enhancement in the past few years, yet the enhancement performance on real-world depth images is still largely unsatisfactory. Two main reasons are the complexity of realistic noise and degradation in depth images, and the difficulty in collecting noise-clean pairs for supervised enhancement learning. This work aims to develop a self-supervised learning method for RGB-guided depth image enhancement, which does not require any noisy-clean pairs but can significantly boost the enhancement performance on real-world noisy depth images. To this end, we exploit the dependency between RGB and depth images to self-supervise the learning of the enhancement model. It is achieved by maximizing the cross-modal dependency between RGB and depth to promote the enhanced depth having dependency with the RGB of the same scene as much as possible. Furthermore, we augment the cross-modal dependency maximization formulation based on the optimal transport theory to achieve further performance improvement. Experimental results on both synthetic and real-world data demonstrate that our method can significantly outperform existing state-of-the-art methods on depth denoising, multi-path interference suppression, and hole filling. Particularly, our method shows remarkable superiority over existing ones on real-world data in handling various realistic complex degradation. Code is available at https://github.com/wjcyt/SRDE.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
仁爱柠檬完成签到,获得积分10
1秒前
科研通AI2S应助wws采纳,获得10
3秒前
6秒前
JackMa应助略略略爱采纳,获得30
6秒前
现代的自行车完成签到 ,获得积分10
6秒前
111完成签到 ,获得积分10
8秒前
坦率的惊蛰完成签到,获得积分10
8秒前
王彤彤发布了新的文献求助10
11秒前
gnr2000应助wws采纳,获得10
13秒前
孤独的丸子头完成签到,获得积分10
15秒前
16秒前
称心天思完成签到,获得积分20
17秒前
18秒前
如意的汽车完成签到,获得积分10
19秒前
隐形曼青应助zhanghaha采纳,获得10
19秒前
21秒前
21秒前
23秒前
25秒前
26秒前
27秒前
27秒前
爱你的歌发布了新的文献求助10
28秒前
Dr大壮发布了新的文献求助10
29秒前
王彤彤完成签到,获得积分10
32秒前
34秒前
冷傲似狮完成签到,获得积分10
35秒前
35秒前
36秒前
CodeCraft应助仁爱柠檬采纳,获得10
36秒前
Yuan发布了新的文献求助30
37秒前
37秒前
38秒前
SciGPT应助笑林采纳,获得10
40秒前
lai完成签到 ,获得积分10
40秒前
zhanghaha发布了新的文献求助10
40秒前
41秒前
Cilia发布了新的文献求助10
41秒前
41秒前
lime发布了新的文献求助10
42秒前
高分求助中
Spray / Wall-interaction Modelling by Dimensionless Data Analysis 2000
Evolution 3rd edition 1500
保险藏宝图 1000
Lire en communiste 1000
Mantiden: Faszinierende Lauerjäger Faszinierende Lauerjäger 700
PraxisRatgeber: Mantiden: Faszinierende Lauerjäger 700
Mathematics and Finite Element Discretizations of Incompressible Navier—Stokes Flows 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3186351
求助须知:如何正确求助?哪些是违规求助? 2836623
关于积分的说明 8010396
捐赠科研通 2498987
什么是DOI,文献DOI怎么找? 1334049
科研通“疑难数据库(出版商)”最低求助积分说明 637003
邀请新用户注册赠送积分活动 604909