An efficient residual learning deep convolutional neural network for de-noising medical images
Keywords:
image denoising, gaussian noise, residual learning, convolutional neural network, peak signal to noise ratioAbstract
Image denoising is a pre-processing technique that is done in every image processing applications. It plays a significant role in the performance of any methods. The objective of this paper is to remove Gaussian noises at different noise levels in medical images. This paper proposed an efficient Deep Convolution Neural Network model for denoising medical images to remove Gaussian noise using Residual Learning. Convolutional Neural Networks are a class of deep neural networks that can be trained on large databases and have excellent performance on image denoising. Residual learning and batch normalisation are various techniques used to enhance the training process and denoising performance. The proposed RL-DCNN method is tested with 20 layers and evaluated using the performance metrics Peak Signal to Noise Ratio, Mean Square Error and Structural Similarity. It is compared with Denoising Convolutional Neural Network and Shrinkage Convolutional Neural Network models and proved to be better than the other methods.
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