Single image Super Resolution with Infrared Imagery and Multi Step Reinforcement Learning
Abstract
Recent studies have shown that Deep Learning (DL) algorithms can significantly improve Super Resolution (SR) performance.Single image SR is useful in producing High Resolution (HR) images from their Low Resolution (LR) counterparts. The motivationfor SR is the potential to assist algorithms such as object detection, localization, and classification. Insufficient work has beenconducted using Generative Adversarial Networks (GANs) for SR on infrared (IR) images despite its promising ability to increaseobject detection accuracy by extracting more precise features from a given image. This work adopts the idea of a relativistic GANthat utilizes Residual in Residual Dense blocks (RRDBs) for feature extraction, a novel residual image addition, and a PixelTransposed Convolutional Layer (PixelTCL) for up-sampling. Recent work has validated the use of GANs for Visible Light (VL)images, making them a strong candidate. The inclusion of these components produce more realistic and natural features while alsoreceiving superior metric values. Supplemental research applies a multi-agent Reinforcement Learning (RL) algorithm to SingleImage Super-Resolution (SISR), creating an advanced ensemble approach for combining powerful GANs. In our implementationeach agent chooses a particular action from a fixed action set comprised of results from existing GAN SISR algorithms to update itspixel values. The pixel-wise arrangement of agents and rewards encourages the algorithm to learn a strategy to increase theresolution of an image by choosing the best pixel values from each option.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 2020
- Accession Number
- AD1107889
Entities
People
- Kyle T Vassilo
Organizations
- University of Dayton