Bayesian retinex underwater image enhancement

By Peixian Zhuang, Chongyi Li, Jiamin Wu. Engineering applications of artificial intelligence, 2021

357 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/f244a5704fdb60a9c75b17474cdae308291b6370

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Chongyi Li
2. Chunle Guo
3. Runmin Cong
4. Peixian Zhuang
5. Junhui Hou
6. Wenqi Ren
7. Saeed Anwar
8. Xipeng Pan

In Media Technology

1. Runmin Cong
2. Chongyi Li
3. Peixian Zhuang
4. Junhui Hou
5. Chunle Guo
6. Wenqi Ren
7. Saeed Anwar

Citations in the corpus

• Underwater Image Enhancement With Hyper-Laplacian Reflectance Priors (2022, unknown): “R (cid:2) 1 , L1 [6]) outperform single-order gradient reflectance prior ( e −(cid:2) (cid:2) R (cid:2) 1 , VRE [3]) in terms of reflectance estimation accuracy and underwater structure enhancement.”
• Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding (2021, methodology): “methods [2]–[8] mainly focus on the accurate estimation of medium transmission.”
• Underwater Image Enhancement via Weighted Wavelet Visual Perception Fusion (2024, methodology): “Although CBAF [16], BRUE [33], UIECˆ2-Net [50], and SGUIE-Net [2] can correct green distortion well, they all introduce slightly red distortion.”