Image blur detection and removal have been one of the major topics of research in image processing in the recent years. These blur detection and removal algorithms have many real world applications including image restoration and image enhancement.
Image blur can include motion blur and out-of-focus blur or blurring due to lens imperfections. This paper also proposes an approach for motion blur detection and removal involving Convolutional Neural Network(CNN) and Generative Adversarial Network(GAN). Check detailed information on de-blurring images using convolutional neural networks.
One of the frequently encountered problems in photography as well as capturing a video is the introduction of blur either due to object movement or camera motion associated with the speed of the camera (shutter speed) when pictures are taken.
Blur is the smoothing of the image pixels essentially resulting in a relatively obscure image.
After this the blur is classified into general blur and motion blur The last step is the image restoration step where blur is removed.
This paper discusses some approaches used to detect and remove motion blur. This paper also proposes a deep learning technique.
System Architecture for GAN-Based Model for Document Image Deblurring
H/w and S/W requirements
Computer : System.
Ram : 1GB
Rom : 32GB
Technology : Machine Learning.
Front End : GUI-tkinter.
IDLE : python 3.10.4
Virtual Envs : Anaconda