PMID- 37224376
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230604
LR  - 20230604
IS  - 1941-0042 (Electronic)
IS  - 1057-7149 (Linking)
VI  - 32
DP  - 2023
TI  - Noise Prior Knowledge Informed Bayesian Inference Network for Hyperspectral 
      Super-Resolution.
PG  - 3121-3135
LID - 10.1109/TIP.2023.3278080 [doi]
AB  - Well-known deep learning (DL) is widely used in fusion based hyperspectral image 
      super-resolution (HS-SR). However, DL-based HS-SR models have been designed 
      mostly using off-the-shelf components from current deep learning toolkits, which 
      lead to two inherent challenges: i) they have largely ignored the prior 
      information contained in the observed images, which may cause the output of the 
      network to deviate from the general prior configuration; ii) they are not 
      specifically designed for HS-SR, making it hard to intuitively understand its 
      implementation mechanism and therefore uninterpretable. In this paper, we propose 
      a noise prior knowledge informed Bayesian inference network for HS-SR. Instead of 
      designing a "black-box" deep model, our proposed network, termed as BayeSR, 
      reasonably embeds the Bayesian inference with the Gaussian noise prior assumption 
      to the deep neural network. In particular, we first construct a Bayesian 
      inference model with the Gaussian noise prior assumption that can be solved 
      iteratively by the proximal gradient algorithm, and then convert each operator 
      involved in the iterative algorithm into a specific form of network connection to 
      construct an unfolding network. In the process of network unfolding, based on the 
      characteristics of the noise matrix, we ingeniously convert the diagonal noise 
      matrix operation which represents the noise variance of each band into the 
      channel attention. As a result, the proposed BayeSR explicitly encodes the prior 
      knowledge possessed by the observed images and considers the intrinsic generation 
      mechanism of HS-SR through the whole network flow. Qualitative and quantitative 
      experimental results demonstrate the superiority of the proposed BayeSR against 
      some state-of-the-art methods.
FAU - Dong, Wenqian
AU  - Dong W
FAU - Qu, Jiahui
AU  - Qu J
FAU - Xiao, Song
AU  - Xiao S
FAU - Zhang, Tongzhen
AU  - Zhang T
FAU - Li, Yunsong
AU  - Li Y
FAU - Jia, Xiuping
AU  - Jia X
LA  - eng
PT  - Journal Article
DEP - 20230602
PL  - United States
TA  - IEEE Trans Image Process
JT  - IEEE transactions on image processing : a publication of the IEEE Signal 
      Processing Society
JID - 9886191
SB  - IM
EDAT- 2023/05/24 19:13
MHDA- 2023/05/24 19:14
CRDT- 2023/05/24 15:03
PHST- 2023/05/24 19:14 [medline]
PHST- 2023/05/24 19:13 [pubmed]
PHST- 2023/05/24 15:03 [entrez]
AID - 10.1109/TIP.2023.3278080 [doi]
PST - ppublish
SO  - IEEE Trans Image Process. 2023;32:3121-3135. doi: 10.1109/TIP.2023.3278080. Epub 
      2023 Jun 2.