Published Paper Details:

The goal of this paper is to boost the resolution of images using Convolutional Neural Networks (CNN) along with the auto-encoder layers. This method trains the CNN model with pairs of low-resolution and high-resolution images so that it can learn to improve the pixel values of low-quality images. The pixel values of low-intensity pixels are replaced with high-intensity pixels and a high-resolution image is generated. The intention of the system is to provide effective enhanced images that can be used in a variety of fields including medicine, satellite, and surveillance images. We propose our own dataset and code to emphasize the focus on the novelty while ensuring the efficacy. Having conducted a number of tests, the project brings forth enhancement in the quality of the reconstructed image which depicts the superiority of auto-encoders and CNNs as promising tools for further developments in super-resolution imaging technology.