Image colorization using Scaled-YOLOv4 detector
Hesham, M., Khaled, H., Faheem, H. (2021). Image colorization using Scaled-YOLOv4 detector. EKB Journal Management System, 21(3), 107-118. doi: 10.21608/ijicis.2021.92207.1118
Mennatullah Hesham; Heba Khaled; Hossam Faheem. "Image colorization using Scaled-YOLOv4 detector". EKB Journal Management System, 21, 3, 2021, 107-118. doi: 10.21608/ijicis.2021.92207.1118
Hesham, M., Khaled, H., Faheem, H. (2021). 'Image colorization using Scaled-YOLOv4 detector', EKB Journal Management System, 21(3), pp. 107-118. doi: 10.21608/ijicis.2021.92207.1118
Hesham, M., Khaled, H., Faheem, H. Image colorization using Scaled-YOLOv4 detector. EKB Journal Management System, 2021; 21(3): 107-118. doi: 10.21608/ijicis.2021.92207.1118

Image colorization using Scaled-YOLOv4 detector

International Journal of Intelligent Computing and Information Sciences
Article 17, Volume 21, Issue 3, November 2021, Page 107-118  XML PDF (1.44 MB)
Document Type: Original Article
DOI: 10.21608/ijicis.2021.92207.1118
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Authors
Mennatullah Hesham email 1; Heba Khaledorcid 2; Hossam Faheem3
1Department of Computer Systems , Faculty of Computer and Information Sciences, Ain Shams University Cairo, Egypt
2Department of Computer Systems, Faculty of Computer & Information Sciences, Ain Shams University, Abbasia, Cairo 11566, Egypt
3Professor of Computer Systems, Computer Systems Department, Faculty of Computer and Information Sciences, Ain Shams University
Abstract
Image Colorization is the problem of defining colors for grayscale images. Recently many research works have been conducted to propose fully-automatic colorization methods. However, many of these papers failed in colorizing images with multiple objects accurately. This might be because of dealing with the whole multi-object image as a single input. Following the efforts made in the last few years, this paper aims at studying the effect of preceding the image colorization with an object detection phase, such that the colorization will be made for each object individually as well as the full image. After the colorization of each object and the full image, they are fused together to reach a more accurate colorized image. In our work, we used a more accurate detector (Scaled-YOLOv4) than that used by the state of the art to increase the quality of the colorization results. Comparing our results to literature, it is found that using Scaled-YOLOv4 increases the Peak signal-to-noise ratio (PSNR) by 2.6%. Results of colorized images with different extensions are compared, and png extension got 5.8% better value of Learned Perceptual Image Patch Similarity (LPIPS) metric than JPEG.
Keywords
Image colorization; Computer vision; CNN; Scaled-YOLOv4; Deep learning
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