Enhancing the low quality images using Unsupervised Colour Correction Method

By Kashif Iqbal, M. Odetayo, Anne E. James, R. A. Salam, A. Talib. IEEE International Conference on Systems, Man and Cybernetics, 2010

477 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/f04718a66cb01fa2c56257ef24735c426de8b4f5

How researchers see this paper

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

In Atomic and Molecular Physics, and Optics

1. Dacheng Tao
2. Wenqi Ren
3. Yudong Wang
4. Huan Gao
5. Chunli Zhu
6. Huihui Yue
7. Jichang Guo
8. Chongyi Li

In Computer Vision and Pattern Recognition

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

In Media Technology

1. Chongyi Li
2. Runmin Cong
3. Chunle Guo
4. Junaed Sattar
5. M. Islam
6. Junhui Hou
7. Chunli Zhu
8. Liheng Bian

Citations in the corpus

• Underwater image enhancement via extended multi-scale Retinex (2017, methodology): “Iqbal et al. [5] proposed an unsupervised color correction method (UCM) to deal with the reduced contrast and non-uniform color cast in the underwater images brought by the scattering and absorption effects underwater.”
• Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding (2021, methodology): “Early attempts aim to adjust the pixel values for visual quality improvement, such as dynamic pixel range stretching [23], pixel distribution adjustment [24], and image fusion [9], [25], [26].”
• An Underwater Image Enhancement Benchmark Dataset and Beyond (2019, background): “[26] stretched the dynamic pixel range in RGB color space and HSV color space to improve the contrast and saturation of an underwater image.”
• PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators (2023, background): “This adjustment is generally performed in the spatial domain, such as dynamic pixel range stretching [12], pixel distribution adjustment [13], and image fusion [14],”
• Generalization of the Dark Channel Prior for Single Image Restoration (2018, methodology): “Motivated by [28], [29], we calculate color correction coefficients φc as”
• Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception (2020, methodology): “Figure 6: Qualitative comparison of Deep SESR-enhanced images with SOTA models: RGHS [29], UCM [33], MSFusion [4], MS-Retinex [72], Water-Net [43], UGAN [20], Fusion-GAN [44], and FUnIE-GAN [35].”
• Real-World Underwater Enhancement: Challenges, Benchmarks, and Solutions Under Natural Light (2019, methodology): “UCM [20] Unsupervised color balance and histogram stretching R Histogram distribution X”
• UIEC^2-Net: CNN-based Underwater Image Enhancement Using Two Color Space (2021, methodology): “Iqbal et al. [44], [45] used histogram stretching process on two different color spaces.”
• Underwater Image Enhancement by Dehazing With Minimum Information Loss and Histogram Distribution Prior (2016, unknown): “Iqbal et al. [16] proposed an unsupervised color correction method (UCM) to achieve underwater image enhancement based on color balance and histogram stretching.”
• Using labeled data to evaluate change detectors in a multivariate streaming environment (2009, unknown): “FastSCNN [18] adopts the skip connection in the deep convolutional neural network and proposes a shallow learning module to downsam-ple for fast and efficient multi-branch low-level feature extraction.”
• U-Shape Transformer for Underwater Image Enhancement (2021, methodology): “Among them, visual prior-based UIE methods [2, 11, 13, 17, 18, 29] mainly concentrated on improving the visual quality of underwater images by modifying pixel values from the perspectives of contrast, brightness and saturation.”