# Median Filter Aided CNN Based Image Denoising:
## 1 Introduction  
Image denoising refers to the recovery of a noise-free digital image from one that has been contaminated by noise. The presence of noise in images can act as a deterrent in the course of various tasks in image processing, like facial recognition [1], form processing [2], environmental remote sensing [3], etc. Thus, the efficient removal of noise from those images at the first stage is extremely crucial to the entire process, otherwise the final result obtained is erroneous. Noise can be of different types, with the most common ones being Gaussian noise and salt-and-pepper noise, which is actually sparsely occuring white and black pixels on an image. The other types of noises are Poisson noise and impulse noise. Poisson noise, which is also known as shot noise, is generated through the Poisson process or Poisson random measures. Impulse noise is a variation of color or brightness on an image. The variation happens randomly and it usually takes place in the image sensors of digital cameras. That is why it is also known as electronic noise. In this paper, the removal of additive white Gaussian noise from images is focused on. This type of noise is a random value that is drawn from the normal (Gaussian) distribution that is superimposed on the clean pixels to obtain the noisy image. This noisy image is then fed into our model in an attempt to recover the noise-free image. The performance of our model is evaluated by comparing the output image to the input noisy image.  
There are quite a few challenges that are faced by researchers in the process of image denoising. The quality of the images coupled with the level of noise make it difficult for the original image to be recovered from the noisy image. Further, the contextual data at different noisy regions in an image might be different and, thus, an appropriate methodneeds to be devised to distinguish all such noisy regions in an image. However, a single model may not be able to denoise all of the noisy pixels. To this end, we propose an ensemble learning approach to combine the denoising capabilities of three models, so that the final model's image denoising capability is better than the individual models and the output is optimal with respect to the results that are produced when compared to those produced by the individual models. Further, we ensure that the individual models are designed so that a variety of noisy pixels can be detected in the image and the image can be appropriately denoised.  
The entire paper is organized as follows: in Section 2, we discuss the related work. In Section 3, we describe the steps of our image denoising model in details. In Section 4, level-wise experimental results are presented, and, finally, in Section 5, the paper is concluded. In this paper, our main contributions are as follows:  
1. Median filter layers are added to ADNet [4] up to the Sparse Block or SB along with a dilation rate of 8.
2. Dilated convolutional layers are used in IRCNN [5] up to a dilation rate of 6, along with median filter layers for it.
3. Median Filter layers are added up to half of the convolutional layers in DnCNN [6].
4. An ensemble of the said models is formed and proposed by using weighted average of the output of each model in order to generate the final denoised image. We take \(\frac{2}{\Pi}\)th part of ADNet output, \(\frac{3}{\Pi}\)th part of IRCNN model and \(\frac{6}{\Pi}\)th part of DnCNN model.