/Color-Chart-Measurement

Measure CIELAB chroma difference and CIELAB color difference by color chart

Primary LanguagePython

Color Chart Difference Measurement

Description

Measure CIELAB chroma difference and CIELAB color difference by color chart.

Getting Started

Run the Demo through Executable File

  • UNZIP demo.zip and execute "demo.exe"
  • In step 1, click "open" button. Select and open the color chart image to be tested.
  • In step 1, click to select the four corner points of the color chart area on the displayed image. Then click "confirm" button.
  • In step 2, the cropped color chart area will be displayed. If it does not correspond to the position of the standard color block , click "Rotate 90" button to adjust it to be consistent with the standard color chart.
  • In step 3, after the color blocks are aligned, click "Calculate" button to calculate the indicators. Then results are displayed on the right.

Note

  • Button "reset": reset the selected corner points in the image.
  • "block scale"：indicates the area of each color block used for calculation. After setting, you need to click "ok" button. The value should be (0,1].
  • For standard color chart, please refer to the display interface.

Run the Demo through Python Code

Install Requriements

  • python 3.7
  • PyQt5
  • opencv-python
  • numpy
  • scipy

Run

python main.py

Underwater Image Enhancement Method

UDCP

Paper & Code

[1] Paulo LJ Drews, Erickson R Nascimento, Silvia SC Botelho, and Mario Fernando Montenegro Campos, “Underwater depth estimation and image restoration based on single images,” IEEE computer graphics and applications, vol. 36, no. 2, pp. 24–35, 2016.

RB

Paper & Code

[2] Xueyang Fu, Peixian Zhuang, Yue Huang, Yinghao Liao, Xiao-Ping Zhang, and Xinghao Ding, “A retinex-based enhancing approach for single underwater image,” in 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014, pp. 4572–4576.

FB

Paper & Code

[3] Codruta O Ancuti, Cosmin Ancuti, Christophe De Vleeschouwer, and Philippe Bekaert, “Color balance and fusion for underwater image enhancement,” IEEE Transactions on Image Processing, vol. 27, no. 1, pp. 379–393, 2017.

UGAN

Paper & Code or Code

[4] Cameron Fabbri, Md Jahidul Islam, and Junaed Sattar, “Enhancing underwater imagery using generative adversarial networks,” in 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018, pp. 7159–7165.

Sea-thru

Paper & Code

[5] Derya Akkaynak and Tali Treibitz, “Sea-thru: A method for removing water from underwater images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 1682–1691.

FUnIEGAN

Paper & Code

[6] Md Jahidul Islam, Youya Xia, and Junaed Sattar, “Fast underwater image enhancement for improved visual perception,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 3227–3234, 2020.

Proposed UICRN demo

Download path: UICRN_demo

Getting Started

  • UNZIP UICRN_demo.zip and execute "./demo/demo.exe"
  • Select model from "./pt/UICRN_best.pt"
  • Open image from file path
  • Run model