A Survey on the New Generation of Deep Learning in Image Processing <s> 5) LAPGAN <s> In this paper we introduce a generative parametric model capable of producing high quality samples of natural images. Our approach uses a cascade of convolutional networks within a Laplacian pyramid framework to generate images in a coarse-to-fine fashion. At each level of the pyramid, a separate generative convnet model is trained using the Generative Adversarial Nets (GAN) approach [11]. Samples drawn from our model are of significantly higher quality than alternate approaches. In a quantitative assessment by human evaluators, our CIFAR10 samples were mistaken for real images around 40% of the time, compared to 10% for samples drawn from a GAN baseline model. We also show samples from models trained on the higher resolution images of the LSUN scene dataset. <s> BIB001 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> 5) LAPGAN <s> Since the proposal of big data analysis and Graphic Processing Unit (GPU), the deep learning technology has received a great deal of attention and has been widely applied in the field of imaging processing. In this paper, we have an aim to completely review and summarize the deep learning technologies for image denoising proposed in recent years. Morever, we systematically analyze the conventional machine learning methods for image denoising. Finally, we point out some research directions for the deep learning technologies in image denoising. <s> BIB002
LAPGAN uses a cascade of CNN within a Laplacian pyramid framework to generate images in a coarse-to-fine processing way BIB001 . At each level of the pyramid, a separate generative ConvNet model can be trained using the GAN approach. The motivation of LAPGAN is to generate highquality sample images by taking random vectors as input. furthermore, LAPGAN can construct by combination CGAN (see in Fig.11 ) and laplacian pyramid framework. CGAN can consider that both the generator and discriminator are conditioned on some extra information y, and y could be any auxiliary information, such as class labels or data from other modalities. One can perform the conditioning by feeding y into both the discriminator and generator as the additional input layer. Then the objective function of a two-player minimax game would be formulated as follows, In the generator, the prior input noise and y can be combined with joint hidden representation. Moreover, in the discriminator, x and y are presented as inputs and to a discriminative function. The Laplacian pyramid is a linear invertible image representation. For example, if the number of levels in the pyramid set to 2 and the sample factor equals 2, then we can obtain the following Fig.12 for image I . Intuitively, each level captures image structure present at a particular scale. For a better understanding of LAPGAN, we can define the difference image at each level, where k is the number of levels in the pyramid, that is, k = 0, 1, 2. Especially, h 2 = I 2 is not a difference image, but a low-frequency residual equal to the final pyramid level. Correspondingly, LAPGAN models at all levels except the final level are conditional generative models that take an upsampled version of the current image l k as a conditioning variable, in addition to the noise vector z k . Furthermore, the advantage lies in the independent training of each pyramid level(see in Fig.13 ). Finally, in the testing phase, the recurrence starts by taking l 2 = 0 and using the model at the final level generator G 2 to generate a difference imageh 2 using noise vector z 2 . Obviously, we have I 2 ≈h 2 by Eq. BIB002 . Further, l 1 can be approximated by up-samplingh 2 , that is, where upsampling is the process of inserting zero-valued samples between original samples to increase the sampling rate. Similarly, one can use generator G 1 to generate a difference imageh 1 , and by Eq. (35), Finally, we can use generator G 0 to generate a difference imageh 0 , and the final high-quality image can be obtained by the following formulations, From the above, LAPGAN can be trained by unsupervised learning.