BLIND RESTORATION USING CONVOLUTION NEURAL NETWORK

Authors

  • Meryem H. Muhson meryem-hussien conan conan
  • Ayad A. Al-Ani

DOI:

https://doi.org/10.31987/ijict.1.1.178

Keywords:

Image restoration, Blind Deconvolution, Deep Learning, deep convolutional neural networks

Abstract

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.

 

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Published

2021-12-15

How to Cite

Muhson, M. H., & Al-Ani, A. A. (2021). BLIND RESTORATION USING CONVOLUTION NEURAL NETWORK. Iraqi Journal of Information and Communication Technology, 1(1), 25–32. https://doi.org/10.31987/ijict.1.1.178