Noise removal is one of the chronic problems while dealing
with the images. Such a noise level would be unacceptable in a photograph since
it would be impossible even to determine the subject. Denoising plays a major
role in retrieving back original signal from noisy observations. In this paper,
we propose Futuristic Flask with Convolution Neural Network (FFCNN), a residual
learning model in deep convolutional neural network which is trained with large
dataset, showing up excellent results for removing Gaussian noise from digital
Images. FFCNN is designed to offer presentation metrics for a user in addition
to performance measurements for neural model training, using the
"Flask" microweb framework to enable interactions. The architecture's
feed-forward denoising neural network structure performs discriminative
learning for picture denoising, specifically for Additive White Gaussian Noise
(AWGN) with a specified noise level and also for blind Gaussian noise. The
proposed algorithm is optimized and speeded by layers of batch normalization
with GPU computing. The resulting PSNR and SSIM obtained are excellent proving
efficiency and effectiveness of the model for several general image denoising
task.
Author(s) Details:
Eldho Paul,
Christ (Deemed to be University), Bangalore, India.
Mugeshbabu
Arulmani,
KAAR
Technology, Chennai, India.
Harish Seshamoorthy,
Sona College of Technology, Salem, India.
Please see the link here: https://stm.bookpi.org/CPSTR-V9/article/view/14170
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