Futuristic flask with convolutional neural network for removing Gaussian noise from digital images
Eldho Paul, Mugeshbabu Arulmani, Harish Seshamoorthy · AIP conference proceedings · 2022
Noise removal is one of the chronic problems while dealing with the images. 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 not only intended for training neural model with performance metrics but also intended to have presentation metrics for a user, using a micro web framework “Flask” to enable interactions. The architecture has feed- forward denoising neural network structure that conducts discriminative learning for image denoising especially for Additive White Gaussian Noise (AWGN) with specific 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.