Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/5149
Title: Deep Learning Based Super Resolution Techniques on Infrared Images
Authors: Rauta, Aradhana
Sahoo, Ajit Kumar
Keywords: Super Resolution
Infrared Image
Convolutional Neural Network
Generative Adversarial Network
Issue Date: Mar-2025
Citation: IEEE International Conference on Advanced Computing Technologies(ICoACT), Sivakasi, India , 14-15 March 2025
Abstract: This paper provides deep learning techniques for Super Resolution (SR) of a single infrared image. Recent advancements in deep learning have significantly enhanced SR techniques. Infrared (IR) image super-resolution holds great potential for improving applications like object detection and classification by recovering finer image details. In this work, we propose a Generative Adversarial Network (GAN)-based model tailored for IR image SR utilizing residual blocks, subpixel convolutions and batch normalization layers for efficient feature extraction and upscaling. The loss function incorporates perceptual loss to encourage realistic outputs and total variation (TV) loss to reduce noise and ensure smoothness while preserving edges. The proposed method is compared against state-of-the-art methods demonstrating superior performance in recovering high-quality IR images.
Description: Copyright belongs to the proceeding publisher.
URI: http://hdl.handle.net/2080/5149
Appears in Collections:Conference Papers

Files in This Item:
File Description SizeFormat 
2025_ICoACT_ARauta_Deep.pdf778.28 kBAdobe PDFView/Open    Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.