Keywords
How to Cite
Abstract
Machine learning is a subset of deep learning. Deep learning techniques have become increasingly popular in recent years for a variety of applications. Many techniques are used in image processing for image enhancement, object detection, and a variety of other purposes. Many deep learning techniques are important in single-image super-resolution (SISR). Many models are used for SISR, and they produce good perceptual and adversarial results. The procedure involves creating a high-resolution (HR) image from a low-resolution (LR) image, including some popular SISR
methods: "Super-Resolution with Convolutional Neural Network" (SRCNN), "Super-Resolution Residual Network" (ResNet), "Super-Resolution Generative Adversarial Network" (SRGAN), and "Enhanced Super-Resolution Generative Adversarial Network" (ESRGAN). These deep learning models are layered, with each layer performing a different task for an LR image. These methods convert a single LR image into an HR image. A single LR image yields many high-resolution images. This paper creates a dataset of DSLR images for use with the SISR method. Deep learning technology is used to convert an LR image to an HR image. The HR images are then compared to the DSLR images. The "peak signal-to-noise ratio" (PSNR) and the "Structural Similarity Index" (SSI) are used to compare them (SSIM). When compared to the old method, the proposed method produces identical results. SSIM and PSNR are shown for both old and proposed approaches. Both approaches possess almost comparable results. The higher the PSNR value, the better the image quality. The same is the case with the SSIM value
References
Zhang, Richard, Phillip Isola, and Alexei A. Efros. "Colorful image colorization." European conference on computer vision. Springer, Cham, 2016.
Zhang, Lvmin, et al. "Two-stage sketch colorization." ACM Transactions on Graphics (TOG) 37.6 (2018): 1-14.
Al Azzeh, Jamil, et al. "Creating a color map to be used to convert a gray image to color image." International Journal of Computer Applications 153.2 (2016): 31-34.
Kumar, Smriti, and Ayush Swarnkar. "Colorization of gray scale images in l?? color space using mean and standard deviation." 2012 IEEE Students' Conference on Electrical, Electronics and Computer Science. IEEE, 2012.
Haldankar, Govind, Atul Tikare, and Jayprabha Patil. "Converting gray scale image to color image." Proceedings of SPIT IEEE Colloquium and International Conference, Mumbai, India. Vol. 1. No. 189. 1989.
Ramesh, Balasubramaniam, et al. "Can distributed software development be agile?." Communications of the ACM 49.10 (2006): 41-46.
Kock, N., and M. Mayfield. "PLS-based SEM Algorithms: The Good Neighbor Assumption." Collinearity, and Nonlinearity (2015
Karlik, Bekir, and Mustafa Sariöz. "Coloring gray-scale image using artificial neural networks." 2009 2nd International conference on adaptive science & technology (ICAST). IEEE, 2009.
Yu, Lu, et al. "Beyond eleven color names for image understanding." Machine Vision and Applications 29.2 (2018): 361-373
Al-Azzeh, Jamil, Ziad Alqadi, and Mohammed Abuzalata. "Performance Analysis of Artificial Neural Networks used for Color Image Recognition and Retrieving."international Journal of Computer Science and Mobile computing 8.2 (2019): 20-33.
Chary, R., D. Rajya Lakshmi, and K. V. N. Sunitha. "Feature extraction methods for color image similarity." arXiv preprint arXiv:1204.2336 (2012).
Kuo, Cheng-Hao, Sameh Khamis, and Vinay Shet. "Person re-identification using semantic color names and rankboost." 2013 IEEE workshop on applications of computer vision (WACV). IEEE, 2013.
Ansari, Mohd, and Dushyant Kumar Singh. "Significance of Color Spaces and Their Selection for Image Processing: A Survey." Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science) 15.7 (2022): 946-956.
Malviya, Ashwini V., and Siddharth A. Ladhake. "Copy move forgery detection using low complexity feature extraction." 2015 IEEE UP Section Conference on Electrical Computer and Electronics (UPCON). IEEE, 2015.
Anwar, Saeed, et al. "Image colorization: A survey and dataset." arXiv preprint arXiv:2008.10774 (2020).
Huang, Shanshan, et al. "Deep learning for image colorization: Current and future prospects." Engineering Applications of Artificial Intelligence 114 (2022): 105006.
Dhir, Rashi, Meghna Ashok, and Shilpa Gite. "An overview of advances in image colorization using computer vision and deep learning techniques." Review of Computer Engineering Research 7.2 (2020): 86-95.
Royer, Amelie, Alexander Kolesnikov, and Christoph H. Lampert. "Probabilistic image colorization." arXiv preprint arXiv:1705.04258 (2017).
Varga, Domonkos, and Tamás Szirányi. "Fully automatic image colorization based on Convolutional Neural Network." 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016.
Nguyen, Tung, Kazuki Mori, and Ruck Thawonmas. "Image colorization using a deep convolutional neural network." arXiv preprint arXiv:1604.07904 (2016).
En.wikipedia.org. (2019). "Mughal-e-Azam". [online] Available at: https://en.wikipedia.org/wiki/Mughal-e-Azam#Colourisation, 2019.
Bao, Bin, and Hongbo Fu. "Scribble-based colorization for creating smooth-shaded vector graphics." Computers & Graphics 81 (2019): 73-81..
Reddit.com. (2019). " Colorization - The colorization of old black & white photos". [online] Available at: https://www.reddit.com/r/Colorization/, 2019.
He, Mingming, et al. "Deep exemplar-based colorization." ACM Transactions on Graphics (TOG) 37.4 (2018): 1-16.
Zhang, Richard, Phillip Isola, and Alexei A. Efros. "Colorful image colorization."European conference on computer vision. Springer, Cham, 2016.
"Colorize Black and White Photos", Algorithmia, 2019. [Online]. Available: https://demos.algorithmia.com/colorize-photos, 2019.
Reinhard, Erik, et al. "Color transfer between images." IEEE Computer graphics and applications 21.5 (2001): 34-41.
Tai, Yu-Wing, Jiaya Jia, and Chi-Keung Tang. "Local color transfer via probabilistic segmentation by expectation-maximization." 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). Vol. 1. IEEE, 2005.
Welsh, Tomihisa, Michael Ashikhmin, and Klaus Mueller. "Transferring color to greyscale images." Proceedings of the 29th annual conference on Computer graphics and interactive techniques. 2002.
Alim, Affan, et al. "The most discriminant subbands for face recognition: A novel information-theoretic framework." International Journal of Wavelets, Multiresolution and Information Processing 16.05 (2018): 1850040.
Zhang, Richard, Phillip Isola, and Alexei A. Efros. "Colorful image colorization."European conference on computer vision. Springer, Cham, 2016.
"CNN Architectures, a Deep-dive", Medium, 2019. [Online]. Available: https://towardsdatascience.com/cnn-architectures-a-deep-dive-a99441d18049, 2019.
Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Communications of the ACM 60.6 (2017): 84-90.