Authors :
Nikitha Reddy Amaram
Volume/Issue :
Volume 5 - 2020, Issue 12 - December
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/2KTJdA0
Abstract :
Degradation due to haziness, camera defocus
and noise can be corrected using Image restoration. Only
with the understanding of the deteriorating elements one
can obtain the original image. Existing methods of image
restoration have the limitations of suffering from bad
convergence properties; the algorithms converging to
local minima, and being unsuitable for real imaging
applications. Few techniques, moreover, make
constrictive presumptions on the PSF or the true image
thereby limiting the algorithm's flexibility to different
applications. Traditional approach involves de-blurring
filters which are applied on the degraded images without
the understanding of blur and its effectiveness. This
paper is based on the approaches of AI that are applied
for restoration problem in which images are distorted by
a blur function and adulterated by some arbitrary noise.
De-noising is enabled through the use of auto encoders
while de-blurring is done through generative adversarial
networks where a discriminator is used to analyze each
output image given by the generator. The processing of
satellite images is a major application of this proposed
system of image restoration.
Keywords :
Satellite Images, Generative Adversarial Networks, Autoencoders, Image Enhancement, De-Blur, DeNoise.
Degradation due to haziness, camera defocus
and noise can be corrected using Image restoration. Only
with the understanding of the deteriorating elements one
can obtain the original image. Existing methods of image
restoration have the limitations of suffering from bad
convergence properties; the algorithms converging to
local minima, and being unsuitable for real imaging
applications. Few techniques, moreover, make
constrictive presumptions on the PSF or the true image
thereby limiting the algorithm's flexibility to different
applications. Traditional approach involves de-blurring
filters which are applied on the degraded images without
the understanding of blur and its effectiveness. This
paper is based on the approaches of AI that are applied
for restoration problem in which images are distorted by
a blur function and adulterated by some arbitrary noise.
De-noising is enabled through the use of auto encoders
while de-blurring is done through generative adversarial
networks where a discriminator is used to analyze each
output image given by the generator. The processing of
satellite images is a major application of this proposed
system of image restoration.
Keywords :
Satellite Images, Generative Adversarial Networks, Autoencoders, Image Enhancement, De-Blur, DeNoise.
