# Median Filter Aided CNN Based Image Denoising:
## 2 Related Work  
Over the last few decades, a variety of methods have been proposed for image denoising. These include non-linear [7] and non-adaptive filtering methods [8], which help to preserve edge information as well as signal to noise ratio information to estimate the noise. Some methods use wavelet shrinkage for denoising. [PERSON] [9] used an universal threshold for every wavelet coefficient, while [PERSON] et al. used an adaptive approach for wavelet soft thresholding [10], which helps in finding a unique threshold for every wavelet sub-band. Besides, an iterative clipping algorithm is used by [PERSON]'s algorithm (TV-Chambolle) [11] for total variation denoising. All of these methods remove Gaussian noise from images.  
However, such methods require manual tuning of parameters, which is tedious. Hence, machine learning methods were introduced, which overcome the said drawbacks. [PERSON] et al. [12] proposed a sparse-based method using collaborative altering, which uses collaborative altering to support image denoising. Markov Random Field (MRF) based methods are used in [13], which provide competitive results when compared to the methods prior to it. However, such methods often do not support all types of images and, hence, are not flexible enough.  
With the advent of deep learning, neural network based methods [14; 15] were originally proposed. However, such methods are often time consuming for large images and spatial features are lost. Hence, CNNs were introduced for image denoising [16; 17]. Conventional CNNs, as well as the LeNet [18], have real-world application in handwritten digit recognition, but they have certain drawbacks. For instance, they use activation functions, like Sigmoid and Tanh, which result in high computational cost and they also generate vanishing gradients. However, these drawbacks were overcome by AlexNet [19], and then other architectures, like VGG [20] and GoogleNet [21]. After that, many CNN based denoising models have been introduced in literature. In this context, Denoising CNNs (DnCNNs) have proven to be effective, while also efficient, in terms of time. [PERSON] proposed a Color Non-Local Network (CNLNet) [22], which uses the inherent non-local self-similarity on natural images to efficiently perform denoising. For blind denoising, [PERSON] et al. proposed a fast and flexible denoising model known as FFDNet [23], with a tunable noise level as input, to efficiently denoise an image. [PERSON] et al. [24] used a combination of Generative Adversarial Network (GAN) and CNN blind denoiser (GCBD) to first generate the noise samples and then use the noise patches to create a paired training dataset to train a CNN for denoising. All the methods mentioned till now are used for removing Gaussian noise from images. Another blind denoising model (CBDNet) [25] proposed by [PERSON] et al. removes real world noise noise from the given real noisy image by two sub-networks, one in charge of estimating the noise of the real noisy image and the other in charge of obtaining the latent clean image. For denoising images affected with salt-and-pepper noise, the authors in [26] use CNN with median filter layers for denoising. In the work [27], responsive median filters and the modified Harris corner point detector are used for reduction of Poisson noise in X-ray images. For reduction of mixed Gaussian-impulse noise, a novel CNN based denoising method is proposed in [28].  
The mentioned methods perform well overall; however, they often do not span wide enough to cover all types of images. To this end, in the current scope of our work, we propose an ensemble model that can combine the efficiencies of multiple CNN models to build a composite classification model that aims to denoise all varieties of images as far as possible.