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@article{157320,
        author = {Rajesh B and Muralidhara B L and L. Hamsaveni},
        title = {Image Inpainting via Generative Multi-  column with the aid of Deep Convolutional Neural Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {6},
        pages = {643-654},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=157320},
        abstract = {Images can be described as visual representations or likeness of something (person or object) which can be reproduced or captured, e.g. a hand drawing, photographic material. The advent of the digital age has seen the rapid shift image storage technologies, from hard-copies to digitalized units in a less burdensome manner with the application of digital tools. The research aims to design a confidence-driven reconstruction loss while an implicit diversified Markov Random Field (MRF) regularization is adopted to enhance local details. The multi-column network combined with the reconstruction and MRF loss propagates local and global information derived from context to the target inpainting regions. Extensive experiments on challenging street view, face, natural objects and scenes manifest that our proposed method produces visual compelling results even without previously common post-processing. The research involves pre-trained Deep Convolutional Neural Network (DCNN) and their training networks like ResNet50, GoogleNet, AlexNet, VGG-16, resnet18 and densenet201. The average PSNR performance of the ResNet50 model is 25.46db SSIM is 0.929 and MSE is 0.1585, which is superior over comparative techniques.},
        keywords = {Inpainting, Markov Random Field, Deep Convolutional Neural Network, resnet50.},
        month = {},
        }