Determination of Convolutional Neural Network for Removing Gaussian Noise from Digital Images

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.