Single Underwater Image Enhancement Using Integrated Variational Model

Li, Nan; Hou, Guiting; Liu, Yuhai; Pan, Zhenkuan; Tan, Lifeng

doi:10.2139/ssrn.4007963

Cited by 1 publication

(1 citation statement)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To validate the efficacy of the proposed algorithm, this section conducts a comparison between results images using both subjective and objective evaluation under identical conditions. Subjective evaluation images are sourced from the opensource datasets RUIE datasets [28] . Objective evaluation employs the Underwater Color Image Quality Evaluation (UCIQE) [29] and Underwater Image Quality Metrics (UIQM) [30] , comparing the proposed algorithm with classical underwater image restoration algorithms.…”

Section: Image Restoration and Evaluationmentioning

confidence: 99%

Airlight estimation in underwater image restoration

Chu,

Zhang,

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

View full text Add to dashboard Cite

Underwater imaging is plagued by light absorption and scattering, resulting in distorted, blurry, and low-contrast. This paper introduces an innovative underwater image restoration algorithm that combines natural lighting-based airlight estimation with the refined dark channel prior. The algorithm directly estimates airlight, considering various underwater conditions such as depth, water quality, and camera-object distance, using the Jaffe-McGlamery underwater image formation model tailored for real-world underwater scenarios. A transmission map formula rooted in the refined dark channel prior is then derived. Finally, the algorithm employs the estimated airlight and transmission map to restore the image. Experimental results validate the algorithm's effectiveness in removing airlight artifacts, enhancing image contrast, and providing a clearer and more natural visual output. This approach promises to advance the quality of underwater imaging and its applicability across various domains.

show abstract

Section: Image Restoration and Evaluationmentioning

confidence: 99%