EconPapers    
Economics at your fingertips  
 

Image Inpainting via Generative Multi-column with the aid of Deep Convolutional Neural Networks

Rajesh B () and Muralidhara B L ()
Additional contact information
Rajesh B: BASE University, Bengaluru
Muralidhara B L: Department of Computer Science & Application, Bangalore University, Bengaluru

Authors registered in the RePEc Author Service: Rajesh B ()

BASE University Working Papers from BASE University, Bengaluru, India

Abstract: Images can be described as visual representations or likeness of something (person or object or a scanned document) 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 and VGG-16. The average PSNR performance of the proposed model is 24.64db and Structural Similarity Index Measure (SSIM) is 0.9018.

Keywords: Markov Random Field; Deep Convolutional Neural Network; ResNet50; GoogleNet; AlexNet; VGG-16; Structural Similarity Index Measure. (search for similar items in EconPapers)
Pages: 17 pages
Date: 2021-05
New Economics Papers: this item is included in nep-cmp and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://base.ac.in/wp-content/uploads/2021/05/BASE- ... p-Series-07-2021.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:alj:wpaper:07/2021

Access Statistics for this paper

More papers in BASE University Working Papers from BASE University, Bengaluru, India Contact information at EDIRC.
Bibliographic data for series maintained by Indrani ().

 
Page updated 2022-09-30
Handle: RePEc:alj:wpaper:07/2021