Underwater Image Enhancement by Wavelength Compensation and Dehazing

By John Yi-Wu Chiang, Ying-Ching Chen. IEEE Transactions on Image Processing, 2012

1,132 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/932982bb52924bcef38058ca37f9f5a82f93b386

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. Runmin Cong
3. Chunle Guo
4. Peixian Zhuang
5. Wenqi Ren
6. Chunli Zhu
7. Liheng Bian
8. Lintao Peng

Citations in the corpus

• Sea-Thru: A Method for Removing Water From Underwater Images (2019, methodology): “Others [19, 44, 55] alleviate this problem by using fixed attenuation coefficients measured for just one water type.”
• Underwater Image Enhancement by Dehazing With Minimum Information Loss and Histogram Distribution Prior (2016, neutral): “…(SID) method [10], Dark Channel Prior (DCP) method [11], Chromatism-based (CB) underwater image dehazing method [17], Wavelength Compensation and Dehazing (WCID) underwater image enhancement method [18], Histogram Equalization (HE) method, and Probability-based (PB) image enhancement method [8].”
• U-Shape Transformer for Underwater Image Enhancement (2021, methodology): “In addition, physical-model based UIE methods [5, 7, 8, 12, 15, 27, 30, 35, 44] mainly focus on the accurate estimation of medium transmission.”
• A deep CNN method for underwater image enhancement (2017, methodology): “To evaluate the performance, we used following algorithms for comparisons: Wavelength Compensation and Dehazing (WCID) algorithm for underwater image enhancement in [9] ,The Dehazing algorithm (DA) and Contrast enhancement algorithm (CA) in [7].”
• An Underwater Color Image Quality Evaluation Metric (2015, methodology): “Chiang and Chen [31] applied SNR and MSE to measure the performance of their”
• Color Balance and Fusion for Underwater Image Enhancement (2018, methodology): “In the underwater context, the approach of Chiang and Chen [27] segments the foreground and the background regions based on DCP, and uses this information to remove the haze and color variations based on color compensation.”
• Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset (2018, methodology): “They use the same fixed water attenuation as in [13] to recover the scene.”
• UIEC^2-Net: CNN-based Underwater Image Enhancement Using Two Color Space (2021, methodology): “Most physical model-based methods [6], [7] are based on a simplified underwater image formation model [8], which is inaccurate because it assume many parameters.”
• A Revised Underwater Image Formation Model (2018, methodology): “For example, in [5, 12, 27] wideband attenuation coefficients were estimated by picking values at single wavelengths from physical attenuation curves (like those in Fig.”
• Human-Visual-System-Inspired Underwater Image Quality Measures (2016, background): “T HE quality of underwater images plays a pivotal role in ocean engineering and scientific research, such as monitoring sea life, accessing geological environment, and ocean rescue [1].”
• Using labeled data to evaluate change detectors in a multivariate streaming environment (2009, neutral): “ICENet [26] and ICENetv2 [27] show that the two-branch network structure like BiSeNet [11] with a finer spatial branch and a deep context semantic branch is very suitable to segment the river ice with scale diversity characteristic.”
• Automatic Red-Channel underwater image restoration (2015, methodology): “With any of the three possibilities, the hypothesis of the theorem is verified.”
• A Rapid Scene Depth Estimation Model Based on Underwater Light Attenuation Prior for Underwater Image Restoration (2018, background): “where ee−ββ(xx) can be represented as the normalized residual energy ratio NNNNeeNN(λλ), which depends on the wavelength of one channel and the water type in reference to [7].”
• Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding (2021, background): “According to the image formation model in bad weather [47], [48], which is widely used in image dehazing and underwater image restoration algorithms [2], [29], [30], the quality-degraded image can be expressed as: where x indicates the pixel index, I is the observed image, J is the clear image, A…”
• Generalization of the Dark Channel Prior for Single Image Restoration (2018, methodology): “Comparisons of depth estimation based on the DCP [1], [4], [5], [9]–[11], DCP variants [12], [13], [24], and our method for images with different light lighting conditions and color casts.”