Image Demorpher Using Machine Learning: Removing Fake Layers and Restoring Original Images
Keywords:
Image Restoration, Machine Learning, Deep Learning, Neural Networks, Morphed Images, Fake Layers, Image Degradation, Data Set, Accuracy, Fidelity, Image EnhancementAbstract
This project introduces an innovative machine learning-based technique for the restoration of original images that have suffered distortion due to the presence of fake layers or other forms of image degradation. The approach entails the training of a deep neural network to acquire the ability to discern and reconstruct the mapping between morphed images and their corresponding pristine originals. To assess its effectiveness, a comprehensive dataset comprising images bearing diverse layers was meticulously curated, serving as the basis for both training and testing the deep neural network. The results demonstrate the remarkable proficiency of the proposed approach in accurately and faithfully restoring original images from their distorted counterparts. This advancement holds immense promise in the realm of machine learning-driven image restoration, with its potential applications spanning a multitude of fields. By providing a robust solution for image restoration from morphed images, this approach significantly enhances the precision and dependability of image-layered analyses, ultimately contributing to the advancement of numerous domains reliant on image fidelity.
Downloads
References
Rajesh Kochher, Dr Anshu Oberoi, and Dr. Pallavi Goel, “Image restoration on mammography images,” 2016.
Dong, C., Loy, C. C., He, K., & Tang, X. (2015). Image super-resolution using deep convolutional networks,2011.
Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017). Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising.
Huang, J. B., Singh, A., Ahuja, N., & Yang, M. H. (2015). Single image super-resolution from transformed self-exemplars.
Lai, W. S., Huang, J. B., Ahuja, N., & Yang, M. H. (2016). Deep laplacian pyramid networks for fast and accurate super-resolution..
Schuler, C. J., Hertel, L., & Kong, N. (2019). Image-to-image translation for cross-domain disentanglement.
Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities improve neural network acoustic models.
Bevilacqua, M., Roumy, A., Guillemot, C., & Alberi-Morel, M. L. (2012). Low-complexity single-image super-resolution based on nonnegative neighbor embedding.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
