MGAN-CRCM: a novel multiple generative adversarial network and coarse refinement-based cognizant method for image inpainting

Nafiz Al Asad, Md Appel Mahmud Pranto, Shbiruzzaman Shiam, Musaddeq Mahmud Akand, Mohammad Abu Yousuf, Khondokar Fida Hasan, Mohammad Ali Moni

Research output: Contribution to journalArticlepeer-review

Abstract

Image inpainting is a recognized method for restoring the properties of pixels in damaged or incomplete images in computer vision technology. Some recent techniques based on generative adversarial network (GAN) image inpainting have outperformed traditional approaches due to their excellent deep learning capability and adaptability to various image domains. Since residual networks (ResNet) also gained popularity over time due to their property as a generative model, offering better feature representation and compatibility with other architectures, how could we leverage both of these models to result in even greater success in image inpainting? This paper proposes a novel architecture for image inpainting based on GAN and residual networks. Our proposed architecture consists of three models: Transpose Convolution-based GAN, Fast ResNet-Convolutional Neural Network, and Co-Modulation GAN. Transpose Convolution-based GAN is our newly designed architecture. It produces guided and blind image inpainting, and FR-CNN performs the object removal case. Co-Mod GAN acts as a refinement layer because it refines the results from Transpose Convolution-based GAN and FR-CNN. To train and evaluate our proposed architecture on publicly available benchmark datasets: CelebA, Places2, and ImageNet are used. Our approach proves our hypothesis, and our proposed model acquires the highest accuracy of 96.59% in the ImageNet dataset, FR-CNN acquires the highest accuracy of 96.70% in the Places2 dataset, and Co-Mod GAN acquires the highest accuracy of 96.16% in the CelebA dataset. Through an analysis of both qualitative and quantitative comparisons, it is evident that our proposed model exceeds existing architectures in performance.

Original languageEnglish
Article number106789
JournalNeural Computing and Applications
DOIs
Publication statusAccepted/In press - 2025

Fingerprint

Dive into the research topics of 'MGAN-CRCM: a novel multiple generative adversarial network and coarse refinement-based cognizant method for image inpainting'. Together they form a unique fingerprint.

Cite this