A Survey on the New Generation of Deep Learning in Image Processing <s> E. IMAGE COMPRESSION <s> Compressed sensing magnetic resonance imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not only reduce the scanning cost and ease patient burden, but also potentially reduce motion artefacts and the effect of contrast washout, thus yielding better image quality. Different from parallel imaging-based fast MRI, which utilizes multiple coils to simultaneously receive MR signals, CS-MRI breaks the Nyquist–Shannon sampling barrier to reconstruct MRI images with much less required raw data. This paper provides a deep learning-based strategy for reconstruction of CS-MRI, and bridges a substantial gap between conventional non-learning methods working only on data from a single image, and prior knowledge from large training data sets. In particular, a novel conditional Generative Adversarial Networks-based model (DAGAN)-based model is proposed to reconstruct CS-MRI. In our DAGAN architecture, we have designed a refinement learning method to stabilize our U-Net based generator, which provides an end-to-end network to reduce aliasing artefacts. To better preserve texture and edges in the reconstruction, we have coupled the adversarial loss with an innovative content loss. In addition, we incorporate frequency-domain information to enforce similarity in both the image and frequency domains. We have performed comprehensive comparison studies with both conventional CS-MRI reconstruction methods and newly investigated deep learning approaches. Compared with these methods, our DAGAN method provides superior reconstruction with preserved perceptual image details. Furthermore, each image is reconstructed in about 5 ms, which is suitable for real-time processing. <s> BIB001 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> E. IMAGE COMPRESSION <s> We present a learned image compression system based on GANs, operating at extremely low bitrates. Our proposed framework combines an encoder, decoder/generator and a multi-scale discriminator, which we train jointly for a generative learned compression objective. The model synthesizes details it cannot afford to store, obtaining visually pleasing results at bitrates where previous methods fail and show strong artifacts. Furthermore, if a semantic label map of the original image is available, our method can fully synthesize unimportant regions in the decoded image such as streets and trees from the label map, proportionally reducing the storage cost. A user study confirms that for low bitrates, our approach is preferred to state-of-the-art methods, even when they use more than double the bits. <s> BIB002
Image compression plays a crucial role in the transmission and storage. To reduce storage requires considerable memory, and degrade transmission requires high bandwidths, previous methods mainly focus on compressive sensing, which is a technology-based on sparse coding. Recently, image compression systems based on deep CNN architecture have become an active area of research. For example, ADMM-Net with convolution and non-linear transform can achieve high reconstruction accuracy for compressive sensing magnetic resonance imaging, and keeping the computational efficiency of the ADMM algorithm  . Based on deep CNN, a novel CNN architecture can be designed for semantic perceptual image compression, which can generate a map that highlights semantically-salient regions so that they can encode at a higher quality as compared to background regions. To train a deep compression system with significantly lower bitrates, we can design a GAN framework (see in Fig.29 ), which is global generative Compression, which can be designed to learn a generative model over images, which viewed as a decoder for image compression BIB002 . Besides, Global generative Compression can be considered to be a combination of GANs and learned compression. Concretely, with an encoder E and quantizer q, we can encode the real image x to a compressed representation c, that is, Then this latent code c can be concatenated with noise v drawn from a fixed prior distribution P v , to form the input vector z = [c, v] of generator G. Further, G can generate an decompress imagex = G(z) that is consistent with the real image distribution P x . This process can be expressed by the following loss function, where β balances the distortion term against the GAN loss function and entropy terms, and d is a loss that measures how perceptually similarx to x, H is entropy to estimate the average number of bits on representation vector c. Also, a finite quantizer q given in advance. Finally, Global generative compression based on GAN can significantly accelerate the convergence of the network, and preserving the overall image content while generating the structure of different scales BIB001 .