A Survey on the New Generation of Deep Learning in Image Processing <s> 3) SRCNN <s> Deep convolutional neural network has been applied for single image super-resolution problem and demonstrated state-of-the-art quality. This paper presents several prior information that could be utilized during the training process of the deep convolutional neural network. The first type of prior focuses on edges and texture restoration in the output, and the second type of prior utilizes multiple upscaling factors to consider the structure recurrence across different scales. As demonstrated by our experimental results, the proposed framework could significantly accelerate the training speed for more than ten times and at the same time lead to better image quality. The generated super-resolution image achieves visually sharper and more pleasant restoration as well as superior objectively evaluation results compared to state-of-the-art methods. <s> BIB001 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> 3) SRCNN <s> We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality. <s> BIB002 </s> A Survey on the New Generation of Deep Learning in Image Processing <s> 3) SRCNN <s> This paper proposes novel high-precision simulation technology for high-dynamics signal simulators used for performance validation in high-speed application environments, such as the global navigation satellite system or telemetry, tracking, and command systems. The proposed method is based on piecewise Hermite cubic interpolation, and regenerates the satellite-to-earth distance from the available sparse motion data. A convolution equation is derived to implement the interpolation using an interpolation filter. Therefore, distance interpolation can be performed with digital circuitry without having to explicitly solve for the interpolation polynomial using floating-point calculations. The Hermite interpolator design is based on polyphase or Farrow structures, depending on whether the required sample rate conversion (SRC) ratio is integer or fractional, respectively. The implementation complexities of both structures are shown to be acceptable. The proposed technology is validated and compared with existing methods. It is shown that the proposed approach is applicable to arbitrary SRC ratios, maintains the continuity of both distance and velocity, and improves their simulation precision by up to 98.5% and 95.7%, respectively. Additionally, it reduces the computation complexity (additions and multiplications) by up to 90.4% and 87.5%, respectively. <s> BIB003
There have been a few studies of using deep learning techniques for image super resolution  , BIB001 . Especially, the SRCNN can directly learn an end-to-end mapping between the low-resolution image and high-resolution image BIB002 . The mapping represents a deep CNN model that consists of three operations: patch extraction and representation, non-linear mapping, reconstruction. At the beginning of detailing each operation, here we only consider a single low-resolution image, and first upscale it to the desired size using bicubic interpolation; and then denote the interpolated image as y ∈ R m×m×c ; finally, we expect to recover from y an image f (y) that is as similar as possible to the ground truth high-resolution image x ∈ R m×m×c . To keep the following description simple and understandable, we denote y a lowresolution image, and x is the high-resolution image. Firstly, it can be expressed as an operation for the patch extraction and representation: where W 1 and b 1 represent the filters and biases respectively. Specifically, W 1 corresponds to n 1 filters of support where c is the number of channels in the input low-resolution image y ∈ R m×m×c and p 1 is the spatial size of a filter. The biases b 1 ∈ R n 1 . After applying the ReLU max(0, ·) on the filter responses, then the output f 1 (y) is composed of n 1 feature maps, that is, f 1 (y) ∈ R m×m×n 1 . Secondly, for the non-linear mapping, the operation is, where W 2 contains n 2 filters of size p 2 × p 2 × n 1 and b 2 ∈ R n 2 . Without loss of generality, it is possible to add more convolutional layers to increase the non-linearity, but the cost is more training time to increase the complexity of the model. The same with previous operation, that is, f 2 (y) ∈ R m×m×n 2 . Thirdly, for the reconstruction, the final high-resolution image can be generated by the following operation, where W 3 comprise c filters of size p 3 ×p 3 ×n 2 and b 3 ∈ R c , The same with previous operation, that is, f 3 (y) ∈ R m×m×c . Although the above three operations motivate by different intuitions, and they all lead to the same form as a convolutional layer, so three operations together and form a deep CNN architecture, namely SRCNN. Further, the filtering weights and biases are to be optimized by the following loss function, where N is the number of training samples, and parameters We have F(y, ) f 3 (y). Also, using MSE ( mean squared error) as the loss function favors a higher PSNR ( peak signal-to-noise ratio), and the loss function is minimized using SGD with the standard BP algorithm BIB003 . Finally, it is worth pointing out that the ground truth high-resolution images {x n } can prepare as m × m × c-pixel sub-images randomly cropped from the training images. By applying sub-images means, these samples treated as small images rather than patches. Meanwhile, to synthesize the low-resolution samples {y n }, one can blur a sub-image by the Gaussian kernel, sub-sample it by the up-scaling factor, and upscale it by the same factor via bicubic interpolation. We assume that all the convolutional layers have no padding for avoiding the phenomenon of border effects. 