Research Article
Reso-Net: Generic Image Resolution Enhancement Using Convolutional Autoencoders
@INPROCEEDINGS{10.1007/978-3-031-35078-8_25, author={Koustav Dutta and Priya Gupta}, title={Reso-Net: Generic Image Resolution Enhancement Using Convolutional Autoencoders}, proceedings={Intelligent Systems and Machine Learning. First EAI International Conference, ICISML 2022, Hyderabad, India, December 16-17, 2022, Proceedings, Part I}, proceedings_a={ICISML}, year={2023}, month={7}, keywords={Autoencoder Convolutional Neural Network Image Resolution Enhancement Hybrid Model Decoder Encoder Up-Sampling Reconstruction Error}, doi={10.1007/978-3-031-35078-8_25} }- Koustav Dutta
Priya Gupta
Year: 2023
Reso-Net: Generic Image Resolution Enhancement Using Convolutional Autoencoders
ICISML
Springer
DOI: 10.1007/978-3-031-35078-8_25
Abstract
Images are created in a variety of ways in various industries. These images are tough to work with, and as a result, they can’t be used effectively in a variety of fields. In this paper, Image Resolution is improved to carry out the process of generic image enhancement tasks. In this process, the low-resolution image is enhanced so that the high-resolution image is achieved. With the help of Image enhancement, the perception or in other words the process of interpreting information present in images by the human viewers is enhanced and the quality is improved to a large extent. Image resolution augmentation has traditionally been accomplished using a variety of classic image processing approaches. However, these methods are not as robust as they should be in dealing with any form of noise signal associated with the image and unable to handle the problems of Error Control Mechanism, Optimization and some other problems. Therefore, this paper presents a method of image resolution enhancement using Advanced Hybrid Neural Network architecture which brings about significant improvements in the entire process.
