Development of image super-resolution framework

Sanvi Shekar, Kakarla Deepthi, D. Rahul, Dhawan K. S., Vinay V. Hegde


There are some scenarios where the images taken are of low resolution and it is hard to judge the features from them, resulting in the need for enhancement. Super-resolution is a technique to produce a high-resolution image from a lower-resolution image. The intention here is to develop a system that enhances images of faces and satellite images by integrating these models and providing an interface to access this model. There have been various ways of achieving super-resolution using different techniques. Throughout the years, techniques involving deep learning methods, interpolation techniques, and recursive networks have been explored. We find it promising to use generative adversarial networks (GANs). The system has been deployed through Google Collaborate, Python libraries, and the TensorFlow framework. To assess the developed system, which consists of images, three metrics have been calculated. namely, peak signal-to-noise ratio, mean squared error, and structural similarity index. The model successfully demonstrated the capability of GANs by efficiently generating a high-resolution image from a low-resolution image for the given cases. The model would then be run on a standalone server for free Internet access for users to use super-resolution facial images and satellite images.


Generative adversarial network; Mean squared error; Peak signal to noise ratio; Satellite images; Structural similarity index; Super-resolution framework

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