KAKARLA DEEPTHI - Academia.edu (original) (raw)
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University of Illinois at Urbana-Champaign
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Papers by KAKARLA DEEPTHI
IAES International Journal of Robotics and Automation (IJRA)
There are some scenarios where the images taken are of low resolution and it is hard to judge the... more 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 i...
IAES International Journal of Robotics and Automation (IJRA)
There are some scenarios where the images taken are of low resolution and it is hard to judge the... more 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 i...