In Medical imaging, several noises are being found which may disrupt the process of diagnosis, hence the noise has to be filtered and a noise free image has to be reconstructed in image processing such as medical image segmentation, tumour detection, image recognition etc. In this paper a Convolutional Neural Networkmodel in deep learning is used for denoising the image. Weiner filtering, Average filtering, median filtering etc. are some of the traditional image denoising methodologies. The major drawback of this traditional method is, it cannot change the parameters that it uses, hence adaptive methods cannot be used here. In this paper a Convolution Neural Network that uses feed-forward for Denoising (DnCNN) is used which gives good results because of its deep architecture, regularization method and learning algorithm. The proposed approach handle Gaussian Noise, Salt & Pepper noise, Speckle noise and Poison noise for an unknown level than the Denoising Convolution Neural Network, U-Net Denoising Convolution Neural Network and Dilated U-Net Denoising Convolution Neural Network model.

Image Denoising;Poisson noise; Speckle noise;Gaussian noise; Convolutional Neural Network; CNN; DnCNN; UDnCNN;DUDnCN; PSNR; SSIM.