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Text transcribed from the image:
(xdg
WT
CVPR
SEATTLE, WA JUNE 17-21, 2024
Award
Candidate
Image Processing GNN
Breaking Rigidity in Super-Resolution
Yuchuan Tian, Hanting Chen, Chao Xu, Yunhe Wang*
The Lopsided Nature of SR
Super-Resolution (SR): an image restoration task
Receive a low-resolution (LR) image, and
Return a high resolution (HR) image
•
We calculate the difference of HR & LR:
北京大学
191
PEKING
National Key Lab of General AI, SIST, Peking University Huawei Noah's Ark Lab
Leveraging Graph Flexibility in SR
We hope to leverage degree flexibility of graphs
• DF:=
F-Fatal indicates High-Freq parts
Image Processing GNN (IPG) Architecture
One block shares a pair of graphs
Loal Graph
--Cabal Graph
Experiments
-
=
SR is a lopsided restoration problem
•
High-Frequency: more restoration efforts
Low-Frequency: minimal modifications
Convolution
Self-Attention
KNN Graph
•
High-Freq parts have higher node degrees
Pixels as nodes is the most flexible:
SR x4
CAT-A
31.08
Set5
09052
Set14
B100
07960 27.90
ART (4)
33.04 09051
0755
•
Patch nodes could cause misalignments
However, pixel space is large
GRL-822
HAT (5
32.90 09009 29.14
30.04
27.57 07510
0.7950 2256 02407 2251
Urban100 Manga109
DEN
IPG (Dan)
29.23 07973 28.00 07317 2797
33.15 09062 2924 07973 27.99
07319
28.13 092 32.53
• It's impractical to search through all pixels
(
•
Existing SR Solutions are Rigid
Existing SR measures suffer rigidity in that:
Spatially bounded within a fixed boundary
Number of connected pixels fixed
The lopsided nature of SR is neglected
Space flexibility is maintained via sampling
.
Local sampling (M) + Global Sampling (R)
Search & gather global & local node info. in
an alternative manner
Ling
A
CHAN
Full Connect
KNY
x
31.09 29.19
Thresholding
3134 2936
2025
Deg-Flex Set Set 4 than 1001
W 2924 2010
Our Flex-Degree
20 strategy performs
Detail (Ours)
35 224
28.13
even better than
Table 9. Comparison of degree-dexible graphs against plain KNN Full-Connect
graphs in IPG. SR4 results are imported
CFAT: Unleashing T
Abhisek R
Problem Definition
Contribution and Formulati
999 99
91998-99
Implications
Computational Cost
DA