IMAGE ENHANCEMENT USING DJANGO AND ML

Abstract

Image super-resolution is one of the vital image processing methods that improve the resolution of an image in the field of computer vision. In the last two decades, significant progress has been made in the field of super resolution, especially by utilizing deep learning methods. Image super resolution plays an important role in several fields such as, computer graphics, medical imaging, security, space, and satellite. The main objective of this project is to enhance and improve the resolution of an image, so it can be used beneficially in the fields mentioned before. In this we aim to use pioneers of convolutional neural networks to enhance the resolution of the image, with a focus on comparing the performance of the CNN models to that of a previously established method, sparse coding. The model utilizes a neural network architecture that incorporates convolutional layers to learn and extract features from the low-resolution input image, and then uses these features to reconstruct a high-resolution output image. The sparse coding approach, on the other hand, utilizes a sparse representation of the image to reconstruct the high-resolution output. The results of this study will provide insight into the effectiveness of CNN models for enhancing the resolution of the image and its potential as an alternative to traditional sparse coding methods.