Deep Learning on Image Denoising: An overview <s> Machine learning methods for image denoising <s> Abstract Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research. <s> BIB001 </s> Deep Learning on Image Denoising: An overview <s> Machine learning methods for image denoising <s> We introduce a new problem of generating an image based on a small number of key local patches without any geometric prior. In this work, key local patches are defined as informative regions of the target object or scene. This is a challenging problem since it requires generating realistic images and predicting locations of parts at the same time. We construct adversarial networks to tackle this problem. A generator network generates a fake image as well as a mask based on the encoder-decoder framework. On the other hand, a discriminator network aims to detect fake images. The network is trained with three losses to consider spatial, appearance, and adversarial information. The spatial loss determines whether the locations of predicted parts are correct. Input patches are restored in the output image without much modification due to the appearance loss. The adversarial loss ensures output images are realistic. The proposed network is trained without supervisory signals since no labels of key parts are required. Experimental results on seven datasets demonstrate that the proposed algorithm performs favorably on challenging objects and scenes. <s> BIB002 </s> Deep Learning on Image Denoising: An overview <s> Machine learning methods for image denoising <s> We consider the single image super-resolution problem in a more general case that the low-/high-resolution pairs and the down-sampling process are unavailable. Different from traditional super-resolution formulation, the low-resolution input is further degraded by noises and blurring. This complicated setting makes supervised learning and accurate kernel estimation impossible. To solve this problem, we resort to unsupervised learning without paired data, inspired by the recent successful image-to-image translation applications. With generative adversarial networks (GAN) as the basic component, we propose a Cycle-in-Cycle network structure to tackle the problem within three steps. First, the noisy and blurry input is mapped to a noise-free low-resolution space. Then the intermediate image is up-sampled with a pre-trained deep model. Finally, we fine-tune the two modules in an end-to-end manner to get the high-resolution output. Experiments on NTIRE2018 datasets demonstrate that the proposed unsupervised method achieves comparable results as the state-of-the-art supervised models. <s> BIB003
Machine learning methods comprise supervised and unsupervised learning methods in general. The supervised learning methods BIB001  use the given label to make obtained features closer to target for learning parameters and training the denoising model. To give an example, a given denoising model y = x + µ, where x, y and µ represent the given clean image, noisy image and additive Gaussian noise (AWGN) of standard deviation σ, respectively. From the equation above and Bayesian knowledge, it can be seen that the learning of parameters of the denoising model relies on pair {x k , y k } N k=1 , where x k and y k denote the kth clean image and noisy image, respectively. Also, N is the number of noisy images. This processing can be expressed as x k = f (y k , θ, m), where θ is parameters and m denotes the given noise level. Unsupervised learning methods BIB002 use given training samples to find patterns rather than label matching and finish specific task, such as unpair real low-resolution images BIB003 . The recently proposed Cycle-in-Cycle generative adversarial network (CinCGAN) used two steps to recover a high-resolution image. The first step estimated the high-resolution image as label. The second step exploited the obtained label and loss function to train the super-resolution model.