Interactive Deep Image Colorization of Quality


Authors : A. Amareshwara Sai Nath; Ziaul Haque Choudhury

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/yasrpp7x

Scribd : https://tinyurl.com/5n8n4jsa

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY1599

Abstract : Deep Image Colonization is a pioneering project aimed to revolutionizing the field of automated image colorization, particularly focusing on enhancing grayscale photographs' visual appeal and historical significance. Leveraging advanced deep learning models like VGG16 and UNET GAN, the project seeks to accurately and faithfully Add images in black and white some color. Through meticulous evaluation and comparison of different colorization algorithms, including real-time display of results and batch processing capabilities, the project strives to provide users with a seamless and intuitive experience. Beyond aesthetic enhancement, the project explores the implications of automated image colorization in various domains, from historical image restoration to creative visual storytelling. By evaluating colorization accuracy and refining models for real-world usage, the project aims to contribute to the advancement of image processing technologies. Ultimately, "Interactive Deep Image Colonization of Quality" endeavour to fill the void left by the past and the present, providing monochromatic imagery through vibrant hues and precision colorization techniques.

Keywords : Automated Image Colorization, Grayscale Photographs, Deep Learning Models, VGG16, UNET GAN.

References :

  1. G. Fong, "Effect of tin spectral filtration on organ and effective dose in ct colonography and ct lung cancer screening", Medical Physics, vol. 51, no. 1, p. 103-112, 2023. https://doi.org/10.1002/mp.16836
  2. C. Hsu, C. Hsu, Z. Hsu, T. Chen, & T. Kuo, "Intraprocedure artificial intelligence alert system for colonoscopy examination", Sensors, vol. 23, no. 3, p. 1211, 2023. https://doi.org/10.3390/s23031211
  3. J. Axelrad and R. Cross, "Surveillance for colorectal neoplasia in inflammatory bowel disease: when to stop", The American Journal of Gastroenterology, vol. 118, no. 3, p. 429-431, 2022. https://doi.org/10.14309/ ajg.0000000000002168
  4. M. Raju and B. Rao, "Classification of colon and lung cancer through analysis of histopathology images using deep learning models", Ingénierie Des Systèmes D Information, vol. 27, no. 6, p. 967-971, 2022. https://doi.org/10.18280/isi.270613
  5. T. Majtner, J. Brodersen, J. Herp, J. Kjeldsen, M. Halling, & M. Jensen, "A deep learning framework for autonomous detection and classification of crohnʼs disease lesions in the small bowel and colon with capsule endoscopy", Endoscopy International Open, vol. 09, no. 09, p. E1361-E1370, 2021. https://doi.org/ 10.1055/a-1507-4980
  6. G. Fong, "Effect of tin spectral filtration on organ and effective dose in ct colonography and ct lung cancer screening", Medical Physics, vol. 51, no. 1, p. 103-112, 2023. https://doi.org/10.1002/mp.16836
  7. S. Hyeong, J. Lee, S. Kim, D. Lee, G. Suh, & J. Choi, "Application of endoscopic ultrasound to the descending colon and rectum in normal dogs", Veterinary Radiology & Ultrasound, vol. 64, no. 3, p. 557-565, 2023. https://doi.org/10.1111/vru.13226
  8. S. Semenov, I. Ms, F. O'Hara, S. Sihag, B. Ryan, A. OʼConnoret al., "Addition of castor oil as a booster in colon capsule regimens significantly improves completion rates and polyp detection", World Journal of Gastrointestinal Pharmacology and Therapeutics, vol. 12, no. 6, p. 103-112, 2021. https://doi.org/10.4292/ wjgpt.v12.i6.103
  9. B. Li, X. Wang, Y. Fan, S. Wang, X. Tong, J. Zhanget al., "Evaluation of bmi‐based tube voltage selection in ct colonography: a prospective comparison of low kv versus routine 120 kv protocol", Journal of Applied Clinical Medical Physics, vol. 24, no. 5, 2023. https://doi.org/10.1002/acm2.13955
  10. The article "Enhancing infrared colour reproducibility through multispectral image processing using rgb and three infrared channels" was published in Optical Engineering in 2022. It was written by M. Sobue, H. Okumura, H. Takehara, M. Haruta, H. Tashiro, and K. Sasagawa et al. 10.1117/1.oe.61.6.063107 can be found here.
  11. The article "Semantic‐aware automatic image colorization via unpaired cycle‐consistent self‐supervised network" was published in the International Journal of Intelligent Systems in 2021. It was written by Y. Xiao, A. Jiang, C. Liu, and M. Wang. Where to find 10.1002/int.22667?
  12. "Towards vivid and diverse image colorization with generative colour prior", Y. Wu, X. Wang, Y. Liu, H. Zhang, X. Zhao, & Y. Shan, 2021. The article's DOI is 10.48550/arxiv.2108.08826.
  13. "Eliminating gradient conflict in reference-based line-art colorization", Z. Li, Z. Geng, K. Zhao, W. Chen, & Y. Yang, 2022. 10.48550/arxiv.2207.06095 is the URL to be used.
  14. In the International Journal of Intelligent Systems, D. Wu, J. Gan, J. Zhou, J. Wang, & W. Gao present "Fine-grained semantic ethnic costume high-resolution image colorization with conditional gan" (vol. 37, no. 5, p. 2952-2968, 2021). This link points to 10.1002/int.22726.
  15. The article "Gan-based image colorization for self-supervised visual feature learning" was published in Sensors in 2022. It was written by S. Treneska, E. Zdravevski, I. Pires, P. Lameski, and S. Gievska. There is a 10.3390/s22041599 link.
  16. In the Journal of Engineering Science and Technology Review, O. Verma and N. Sharma published "Efficient colour cast correction based on fuzzy logic" in 2017. The article was published on pages 115-122. The journal article 10.25103/jestr.103.16
  17. Chinese Optics Letters, vol. 10, no. 8, p. 081101-81105, 2012. S. Gao, W. Jin, & L. Wang, "Quality assessment for visible and infrared colour fusion images of typical scenes". 1.10.081101 at https://doi.org/10.3788/col2012
  18. In 2024, Zhang Y. published "Image colorization based on transformer with sparse attention". 10.1117/12.3021490 can be found here
  19. The article "Enhancing infrared colour reproducibility through multispectral image processing using rgb and three infrared channels" was published in Optical Engineering in 2022. It was written by M. Sobue, H. Okumura, H. Takehara, M. Haruta, H. Tashiro, and K. Sasagawa et al. 10.1117/1.oe.61.6.063107 can be found here.
  20. "Gan-based image colorization for self-supervised visual feature learning", Sensors, vol. 22, no. 4, p. 1599, 2022, Treneska, S., Zdravevski, E., Pires, I., Lameski, P., & Gievska, S. There is a 10.3390/s22041599 link.
  21. "Efficient colour cast correction based on fuzzy logic" by O. Verma and N. Sharma was published in the Journal of Engineering Science and Technology Review in 2017. The article can be found on pages 115-122. The journal article 10.25103/jestr.103.16
  22. Applied Mathematics and Sciences an International Journal (Mathsj), vol. 4, no. 1/2, p. 01-16, 2017. A. Grigoryan, A. John, & S. Agaian, "Modified alpha-rooting colour image enhancement method on the two side 2-d quaternion discrete fourier transform and the 2-d discrete fourier transform". The mathsj.2017.4201 doi: 10.5121/mathsj.

Deep Image Colonization is a pioneering project aimed to revolutionizing the field of automated image colorization, particularly focusing on enhancing grayscale photographs' visual appeal and historical significance. Leveraging advanced deep learning models like VGG16 and UNET GAN, the project seeks to accurately and faithfully Add images in black and white some color. Through meticulous evaluation and comparison of different colorization algorithms, including real-time display of results and batch processing capabilities, the project strives to provide users with a seamless and intuitive experience. Beyond aesthetic enhancement, the project explores the implications of automated image colorization in various domains, from historical image restoration to creative visual storytelling. By evaluating colorization accuracy and refining models for real-world usage, the project aims to contribute to the advancement of image processing technologies. Ultimately, "Interactive Deep Image Colonization of Quality" endeavour to fill the void left by the past and the present, providing monochromatic imagery through vibrant hues and precision colorization techniques.

Keywords : Automated Image Colorization, Grayscale Photographs, Deep Learning Models, VGG16, UNET GAN.

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