IMAGE AND VIDEO RESTORATION SYSTEM: A REVIEW

The technology of digital image and video restoration is used to restore the original image data from damaged images.Information is lost in digital photos as a result of noise, blurring, and camera focus issues.Image restoration is important for enhancing the quality of blurry satellite photographs, low-quality video tapes of crime scenes, and pictures taken by unmanned aircraft.This research examines how picture restoration has changed over time and how its methods are finding their way into a wide range of applications to enhance user perception. I. INTRODUCTIONImage restoration is the process of estimating the clean, original image from a corrupted or noisy image.Motion blur, noise, and camera focus issues are just a few examples of corruption.Imaging a point source and using the point source image, known as the Point Spread Function (PSF), to restore the picture information lost to the blurring process is how image restoration is carried out.This reverses the process that blurred the image.In this project we have combined various algorithms used for image restoration using machine learning and artificial intelligence.Each algorithm has unique use and they are interesting and important in their own way.Images and Videos during this Era carry huge importance of the data.We have to be really careful when it comes to storage.There can be confidential files which need to be saved properly, so that they won't get in the wrong hands or will be abused.There is also day to day files or photos specifically which can carry importance such as hospital data for the people who have been taking the treatment for years and they shouldn't be damaged otherwise it may cause certain amounts of misunderstandings.While being careful though, many times it so happens that the image gets damaged due to noise or get corrupted.There are also times when we can't take a picture twice since life moments are unique and unfortunately motion blur or camera misfocus can cause the image to distort or maybe ruin it till some extent.Photos taken during low light conditions are especially not in good quality.Since most of the camera's don't work properly in low light conditions the result is extremely dull and life-less images which can be a problem in most of the cases.By using Image Restoration, we can train a dataset to perform various actions like deblurring the image or maybe try to improve light conditions in an Image and also denoising it which are pretty useful when it comes to image restoration Our Aim in this project was to study various ways to do image restoration and searching for various applications of image restoration using machine learning and artificial intelligence.2Various algorithms we studied were.-Deblurring/Denoising the Low-Resolution Image -Image Super Resolution Under Applicative Noise Based on a Convolutional Neural Network -Using CNN for improving quality of ill-exposed Images.However, we are aware of the fact that an automatic system cannot reliably replace an experienced photographer or original high-quality pictures.In case the conditions are severe or image is damaged too much we may or may not be able to convert it into high resolution image.Filling a specific space-time region with newly synthesised content is known as "video completeness.".It has many applications, including restoration (removing scratches), video editing and special effects workflows (removing unwanted objects), watermark and logo removal, and video stabilization (filling the exterior after shake removal instead of cropping). II. ARCHITECTURE 1. COLORIZATIONWe use the "fully convolutional network" model for our baseline model, which places the fully connected layers by convolutional layers that use up sampling rather than pooling operators.This idea is based on encoder-