Một số task cần thực hiện (update realtime)
0. Mot so bai bao nen doc
Image-Super-Resolution paper 1
Hyperspectral Image-Super-Resolution paper 2
1. Diffusion, Kmeans vs Neural network’
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
https://vsehwag.github.io/blog/2023/2/all_papers_on_diffusion.html
Video:
https://youtu.be/sXBPyKycOX8?list=PL5RHjmn-MVHDMcqx-SI53mB7sFOqPK6gN
https://youtu.be/a4Yfz2FxXiY
Link diffusion practice (need practice)
https://drive.google.com/drive/u/0/folders/1Mnc_C4zePJ7e_sJjhSXh06eJ0A3GMSZP
- cách các mô hình diffusion hoạt động thế nào?
- kết hợp với cluster và dùng AE hoặc neural network thế nào?
- Có rất nhiều paper viết về diffusion, việc của mình nên triển khai các mô hình vào tập dữ liệu của mình.
https://github.com/diff-usion/Awesome-Diffusion-Models
https://github.com/amirhossein-kz/Awesome-Diffusion-Models-in-Medical-Imaging
https://github.com/yulunzhang/awesome-diffusion-low-level-vision
https://github.com/labmlai/annotated_deep_learning_paper_implementations/
tree/master/labml_nn/diffusion/ddpm
https://pub.towardsai.net/stable-diffusion-based-image-compresssion-6f1f0a399202
https://colab.research.google.com/drive/1Ci1VYHuFJK5eOX9TB0Mq4NsqkeDrMaaH?usp=sharing#scrollTo=_VGqzNyhOEbn
https://github.com/Justin-Tan/high-fidelity-generative-compression/
- 02 bài báo liên quan đến chỗ nén dữ liệu
Lossy Image Compression with Conditional Diffusion Models
NEURAL IMAGE COMPRESSION WITH A DIFFUSION BASED DECODER
- Triển khai trực tiếp với tập dữ liệu đang làm.
2. video codec (compression), kết hợp diffusion trong robotics
-
end2end video codec
-
diffusion như thế nào?
-
triển khai vào robotics với satellite ra sao?
3. VPN traffic (bài cũ kết hợp với data aggregation)
- bài cũ viết phân lớp cho VPN traffic trong satellite
- kết hợp các thuật toán data aggregation với cải tiến dùng tree trong này.
- cài đặt các thuật toán liên quan đến chỗ data aggregation trước.
Aggregation Tree Based Data Aggregation Algorithm in Wireless Sensor Networks
Yanhua, Hu, and Zhang Xincai. "Aggregation Tree Based Data Aggregation Algorithm in Wireless Sensor Networks." International Journal of Online Engineering 12.6 (2016).
Aggregation Tree Based Data Aggregation Algorithm in Wireless Sensor Networks
Cluster based topology management mechanism [12-13]
is commonly used in wireless sensor networks, where a
head node is selected in each cluster to manage the nodes
in the same cluster, collect data from the intra-cluster
nodes, and transmit data between inter-clusters. So, the
head node of a cluster must know the semantic of collect-
ed data, which makes it impossible to transmit encrypted
data between the head node with bases.
ESPDA, proposed by Li et al. [14], is an energy effi-
cient secure data aggregation protocol. In this protocol,
sensor nodes generate pattern codes based on the raw data,
and send them to the head node. The head node classifies
the raw data according to the received pattern codes, se-
lects the pattern code subset via pattern comparing algo-
rithms, and requires the selected sensors to transmit the
encrypted data.
SIA, proposed by Przydatek et al. [15], is a secure data
aggregation framework for large scale wireless sensor
networks. In this framework, they defined a kind of nodes,
called aggregators, which aggregate the queried data to
reduce the communication cost of the network. In addition,
they applied effective random sampling and interactive
verifying mechanisms to assure that the data aggregated
by aggregators are the maximal approximation of the real
values.
SecureDAV, proposed by Mahimkar et al. [16], is a se-
cure data aggregation and verification protocol in wireless
sensor networks. This protocol assigns keys for nodes of
the same cluster by applying the elliptic curve based secret
sharing scheme. Each normal node computes the average
value of the intra-cluster data and gives its partial signa-
ture, and the head node collects the signature of other
intra-cluster nodes, give a complete signature to the aver-
age values and send it to the base. Finally, the base veri-
fies the signature via the corresponding public key.
SRDA, proposed by Sanli et al. [17], is a reference data
based secure aggregation protocol. This protocol ascer-
tains the differences by comparing the raw data and the
reference data, and the sensor nodes transmit differential
data rather than the raw data to reduce the communication
overhead. Moreover, Sugandhi et al. [18] proposed a se-
cure and energy-saving data aggregation and authorization
protocol with respect to the risk of leaking information
while aggregating data. Sun et al. [19] proposed a credible
behavior based secure data aggregation and route algo-
rithm by studying the credibility of networks. Wu et al.
[20] designed a pattern code based efficient secure data
aggregation protocol in wireless sensor networks.
These secure data aggregation algorithms have their
specific advantages. However, some of them ignore the
limits of resources in wireless sensor networks, and their
computation complexity is very high, which consumes
much energy while aggregating data; and some of them
only focus the security of the data and ignore the security
while transmitting data between different sensors.
4. FL trong satellite để giảm độ trễ
1
2
3
4
5
6
7
8
9
- liệu có biểu diễn FL vào satellite để giảm độ trễ khi truyền được không?
- cluster trong FL hoạt động thế nào?
- Link tham khao: https://github.com/dophuchao/dophuchao.github.io/blob/master/ipynb_files/FL in satellite.pdf
*note: khi viết báo thì nên viết chi tiết thuật toán chỗ xử lý (preprocessing hoặc data aggregation)
5. High Resolution
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
https://github.com/idealo/image-super-resolution
SwinIR: Image Restoration Using Swin Transformer (official repository)
https://github.com/JingyunLiang/SwinIR
[CVPR'20] TTSR: Learning Texture Transformer Network for Image Super-Resolution
https://github.com/researchmm/TTSR
PyTorch implementation of Image Super-Resolution Using Deep Convolutional Networks (ECCV 2014)
https://github.com/yjn870/SRCNN-pytorch
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network implemented in Keras
https://github.com/deepak112/Keras-SRGAN
Lightweight Image Super-Resolution with Enhanced CNN (Knowledge-Based Systems,2020)
https://github.com/hellloxiaotian/LESRCNN
A Flexible and Unified Image Restoration Framework (PyTorch), including state-of-the-art image restoration model. Such as NAFNet, Restormer, MPRNet, MIMO-UNet, SCUNet, SwinIR, HINet
https://github.com/murufeng/FUIR
PyTorch implementation of Accelerating the Super-Resolution Convolutional Neural Network (ECCV 2016)
https://github.com/yjn870/FSRCNN-pytorch
Official PyTorch code for Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling (HCFlow, ICCV2021)
https://github.com/JingyunLiang/HCFlow
Code for Non-Local Recurrent Network for Image Restoration (NeurIPS 2018)
https://github.com/Ding-Liu/NLRN
Camera Lens Super-Resolution in CVPR 2019
https://github.com/ngchc/CameraSR
Official PyTorch code for Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution (MANet, ICCV2021)
https://github.com/JingyunLiang/MANet
Full paper:
https://github.com/ChaofWang/Awesome-Super-Resolution
simple diffusion: End-to-end diffusion for high resolution images
https://arxiv.org/pdf/2301.11093.pdf
https://github.com/Janspiry/Image-Super-Resolution-via-Iterative-Refinement
https://github.com/IceClear/StableSR
https://github.com/LeiaLi/SRDiff
https://github.com/prs-eth/Diffusion-Super-Resolution
6 Satellite image in DL
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
https://github.com/satellite-image-deep-learning/techniques/blob/master/README.md
Rất nhiều kỹ thuật và các bài toán đã làm ở đây rồi.
https://github.com/ailabteam/Deep-learning-based-object-recognition-
in-multispectral-satellite-imagery-for-real-time-applicatio
https://github.com/chrieke/awesome-satellite-imagery-datasets
https://paperswithcode.com/datasets?q=Satellite-Landscapes%20256%20x%20256
https://lorenz.ecn.purdue.edu/~hjanos/publication/horvath_2019.pdf
Tài liệu tham khảo
Internet
Hết.
