Underwater Image Restoration Based on Image Blurriness and Light Absorption

By Yan-Tsung Peng, Pamela C. Cosman. IEEE Transactions on Image Processing, 2017

1,086 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4e7f959cdaa566b357ce410b0e50762d1c9c8cb9

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. Dacheng Tao
4. Wenqi Ren
5. Ming-Hsuan Yang
6. Runmin Cong
7. Saeed Anwar
8. Luc Van Gool

Citations in the corpus

• An Underwater Image Enhancement Benchmark Dataset and Beyond (2019, methodology): “From left to right are raw underwater images, and the results of Fusion-based [29], Retinex-based [31], Two-step-based [30], UDCP [35], Red Channel [40], Histogram Prior [42], Regression-based [37], Blurriness-based [43], GDCP [38], and reference images.”
• Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding (2021, methodology): “For example, the blurriness prior used in [29] does not always hold, especially for clear underwater images.”
• Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset (2018, methodology): “Essentially, we approximate the cameras’ spectral response as a Dirac delta function, similarly to [20], [33].”
• U-Shape Transformer for Underwater Image Enhancement (2021, methodology): “Among the methods, results of the UIBLA [35] and FUnIE [19] are collectively reddish and yellowish, respectively.”
• Underwater Image Enhancement With Hyper-Laplacian Reflectance Priors (2022, positive): “Peng and Cosman [11] presented a depth estimation method based on image blurriness and light absorption (IBLA) to estimate more accurate background light and underwater scene depth.”
• Underwater Image Enhancement Using a Multiscale Dense Generative Adversarial Network (2020, background): “Image restoration techniques [7]–[11] consider the degradation model to enhance underwater images.”
• A Rapid Scene Depth Estimation Model Based on Underwater Light Attenuation Prior for Underwater Image Restoration (2018, methodology): “[6] found the previous background light estimation methods were not robust for various underwater images, and they proposed a method based on the image blurriness and light absorption to estimate more accurate background light and scene depth to restore color underwater image precisely.”