Human-Visual-System-Inspired Underwater Image Quality Measures

By Karen A. Panetta, Chen Gao, Sos S. Agaian. IEEE Journal of Oceanic Engineering, 2016

1,496 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/64a39d6efee4edef7ca5643798b8905965ec45e6

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. Wenqi Ren
5. Peixian Zhuang
6. Chunli Zhu
7. Liheng Bian
8. Lintao Peng

In Media Technology

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

Citations in the corpus

• PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators (2023, methodology): “In addition, we also use some non-reference metrics for evaluation, including UIQM [45], FDUM [46], UCIQE [47], and CCF [48].”
• U-Shape Transformer for Underwater Image Enhancement (2021, methodology): “For images in the non-reference testing dataset, nonreference evaluation metrics UCIQE [48] and UIQM [34] are employed, in which higher UCIQE or UIQM score suggests better human visual perception.”
• Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset (2018, background): “Since it is impossible to obtain medium-free in situ images, comparisons in the existing literature are either based on photos of a color card taken from a short range, e.g. [13], [16], [23], or based on non-reference image quality metrics [42], [43].”
• Generalization of the Dark Channel Prior for Single Image Restoration (2018, background): “Image Quality Measure, UIQM [34], and Underwater Color Image Quality Evaluation Metric (UCIQE) [35], for which larger values represent higher quality.”
• Fast Underwater Image Enhancement for Improved Visual Perception (2019, methodology): “We present the results in Table II, which indicates that although FUnIE-GAN-UP performs better than CycleGAN, its UIQM values on the the paired dataset are relatively poor.”
• A Rapid Scene Depth Estimation Model Based on Underwater Light Attenuation Prior for Underwater Image Restoration (2018, methodology): “The UIQM is a linear combination of colorfulness, sharpness and contrast and a larger value represents higher image quality.”
• Internet of Underwater Things and Big Marine Data Analytics—A Comprehensive Survey (2020, neutral): “[194], [195].”
• Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception (2020, background): “Their performance is quantitatively evaluated on common test sets of each dataset based on standard metrics [35, 49]: peak signal-to-noise ratio (PSNR) [28], structural similarity measure (SSIM) [66], and underwater image quality measure (UIQM) [55].”
• Real-World Underwater Enhancement: Challenges, Benchmarks, and Solutions Under Natural Light (2019, background): “Among them, HLP increases the values of UCIQE the most, and BCCR is favored by UIQM the most.”
• Color Balance and Fusion for Underwater Image Enhancement (2018, methodology): “Table II provides the associated quantitative evaluation, using three recent metrics: PCQI [64], UCIQE [65], and UIQM [66].”
• An Underwater Image Enhancement Benchmark Dataset and Beyond (2019, background): “A higher UCIQE score indicates the result has better balance among the chroma, saturation, and contrast, while a higher UIQM score indicates the result is more consistent with the human visual perception.”
• Using labeled data to evaluate change detectors in a multivariate streaming environment (2009, neutral): “The visualization results are shown in Fig.”
• Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding (2021, methodology): “For Test-C60 and SQUID that do not have corresponding ground truth images, we employ the no-reference evaluation metrics UCIQE [54] and UIQM [55] to measure the performance of different methods.”
• Underwater Image Restoration Based on Image Blurriness and Light Absorption (2017, methodology): “…most comprehensive comparison to date of underwater image restoration techniques, using no-reference quality assessment tools (BRISQUE [18], UIQM [19], and UCIQE [20]), as well as two full-reference approaches (PSNR and SSIM [21]) based on synthesized underwater images with scaled and shifted…”
• Underwater Image Enhancement Using a Multiscale Dense Generative Adversarial Network (2020, methodology): “To make our results more convincing, we employ two no-reference metrics to evaluate the underwater images, e.g., underwater color image quality evaluation (UCIQE) [39] and underwater image quality measure (UIQM) [40].”
• UIEC^2-Net: CNN-based Underwater Image Enhancement Using Two Color Space (2021, neutral): “5753 according to [59].”