Open AccessBLIND RESTORATION USING CONVOLUTION NEURAL NETWORK
Author(s) -
Meryem H. Muhson,
Ayad A. Al-Ani
Publication year - 2021
Publication title -
iraqi journal of information and communication technology/iraqi journal of information and communication technology
Language(s) - English
Resource type - Journals
eISSN - 2789-7362
pISSN - 2222-758X
DOI - 10.31987/ijict.1.1.178
Subject(s) - image restoration , artificial intelligence , kernel (algebra) , computer science , convolution (computer science) , convolutional neural network , computer vision , image (mathematics) , feature (linguistics) , pattern recognition (psychology) , degradation (telecommunications) , artificial neural network , image processing , mathematics , telecommunications , linguistics , philosophy , combinatorics
Image restoration is a branch of image processing that involves a mathematical deterioration and restoration model to restore an original image from a degraded image. This research aims to restore blurred images that have been corrupted by a known or unknown degradation function. Image restoration approaches can be classified into 2 groups based on degradation feature knowledge: blind and non-blind techniques. In our research, we adopt the type of blind algorithm. A deep learning method (SR) has been proposed for single image super-resolution. This approach can directly learn an end-to-end mapping between low-resolution images and high-resolution images. The mapping is expressed by a deep convolutional neural network (CNN). The proposed restoration system must overcome and deal with the challenges that the degraded images have unknown kernel blur, to deblur degraded images as an estimation from original images with a minimum rate of error.