A novel model for quality enhancement of old images and videos
Sai Tarun Yadav Payavula, Praneeta Siddineni, Nikitha Vadapalli Sai Lasya, M. N. Anil Kumar · AIP conference proceedings · 2024
Image restoration is the process in which a corrupted image is restored to its original quality.Since there are plenty of computer vision applications where images are widely used and this is very important to restore noisy or corrupted images for high visual quality and accuracy of image processing-based applications.We aim at providing multiple techniques that caters to the needs of images of varied quality.The techniques to be implemented in this project for image restoration include different deep learning approaches such as SRCNN, SRGAN, deep learning attributes, Ffmpeg (Fast Forward Moving Pictures Experts Group), and bilateral filter.A unified method is given for various types of inputs as it can repair the damaged images and enhance.Super resolution, dehazing, colorization and inpainting are the blocks through which the input go through and get an escalated output.It can support grayscale and colored images.The application is built using Python data science platform.Enables users to give any image as input and output can be seen on the same screen.For human eye perception, sometimes, output and input may appear same.In order to show the quality improvement, we use statistical measures such as PSNR (Peak signal to noise ratio) and SSIM (Structural similarity index measure).These metrics show the performance improvement with quantitative results for proof of the concept.This project also has provision to enhance short videos clips.As the main goal is refinement.