Comparative Analysis of Image Inpainting Techniques

Author(s)	Sagar Sohrab, Madhura Pethkar, Ayush Dedhia, Anwesha Parida, Ameya Naik
Country	India
Abstract	Image inpainting is a critical task in computer vision, aimed at restoring missing or damaged regions in images. Traditional methods like Patch Match relied heavily on texture synthesis but often struggled with maintaining structural integrity, especially in complex or irregular gaps. The emergence of deep learning has transformed the field, with Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Partial Convolutions (PCNs) demonstrating remarkable improvements. These advanced techniques utilize feature extraction, adversarial training, and attention mechanisms to achieve more realistic and coherent results. This research explores and compares these approaches, assessing their strengths, limitations, and performance across diverse datasets.
Field	Computer > Data / Information
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-04-07
Cite This	Comparative Analysis of Image Inpainting Techniques - Sagar Sohrab, Madhura Pethkar, Ayush Dedhia, Anwesha Parida, Ameya Naik - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.3019
DOI	https://doi.org/10.71097/IJSAT.v16.i2.3019
Short DOI	https://doi.org/g9dpgj

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