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Sustainable Advanced Techniques for Enhancing the Image Process

Sustainable Advanced Techniques for Enhancing the Image Process

Pranjit Das, P. S. Ramapraba, K. Seethalakshmi, M. Anitha Mary, S. Karthick, Boopathi Sampath
Copyright: © 2024 |Pages: 25
ISBN13: 9798369316382|ISBN13 Softcover: 9798369346310|EISBN13: 9798369316399
DOI: 10.4018/979-8-3693-1638-2.ch022
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MLA

Das, Pranjit, et al. "Sustainable Advanced Techniques for Enhancing the Image Process." Fostering Cross-Industry Sustainability With Intelligent Technologies, edited by Brojo Kishore Mishra, IGI Global, 2024, pp. 350-374. https://doi.org/10.4018/979-8-3693-1638-2.ch022

APA

Das, P., Ramapraba, P. S., Seethalakshmi, K., Anitha Mary, M., Karthick, S., & Sampath, B. (2024). Sustainable Advanced Techniques for Enhancing the Image Process. In B. Mishra (Ed.), Fostering Cross-Industry Sustainability With Intelligent Technologies (pp. 350-374). IGI Global. https://doi.org/10.4018/979-8-3693-1638-2.ch022

Chicago

Das, Pranjit, et al. "Sustainable Advanced Techniques for Enhancing the Image Process." In Fostering Cross-Industry Sustainability With Intelligent Technologies, edited by Brojo Kishore Mishra, 350-374. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-1638-2.ch022

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Abstract

This chapter discusses modern techniques for image improvement, including pixel editing, clarity enhancement, and minimal-size object recognition. An outline of photo enhancement and how deep learning could address its issues comes first. Both sophisticated techniques like cut-out and style transfer and frequently used ones like rotation and scaling are covered in this chapter. Additionally included are techniques for manipulating pixels, such as brightness adjustment, colour space conversion, and denoising algorithms. Assisting clarity issues like super-resolution, deblurring, and contrast amplification are also covered in this chapter. In order to address the issues with minimal-size object recognition, the chapter also looks into single-shot detectors and multi-scale networks. Through case studies and applications in medical imaging, autonomous driving, and surveillance systems, the value of these techniques is demonstrated. A discussion of prospective future study areas and how these techniques could affect computer vision and image processing brings the chapter to a close.

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