In: CVPR (2016), Kim, J., Lee, J.K., Lee, K.M. With the domain expertise of the conventional sparse-coding-based method, it outperforms SRCNN with a smaller model size. It is widely observed that depth is the key factor that affects the performance. We also show their performance (average PSNR on Set5) trained on the 91-image dataset[10]. As shown in Fig. 3.4). 4.2), we then train the network for \(\times \)2 on the basis of that for \(\times \)3. Interestingly, the reversed network is like a downscaling operator that accepts an HR image and outputs the LR one. Another 20 images from the validation set of the BSD500 dataset are selected for validation. This also provides us a faster way to upscale an image to several different sizes. Motivated by SRCNN, some problems such as face hallucination[16] and depth map super-resolution[17] have achieved state-of-the-art results. Bibliographic details on Accelerating the Super-Resolution Convolutional Neural Network. Third, we adopt smaller lter sizes but more mapping layers. However, the high . https://doi.org/10.1007/978-3-319-46475-6_25, DOI: https://doi.org/10.1007/978-3-319-46475-6_25, eBook Packages: Computer ScienceComputer Science (R0). Lastly, we adopt smaller filter sizes and less filters (e.g.,from Conv(9,64,1) to Conv(5,56,1)), and obtain a final speedup of \( 41.3\times \). For example, to upsample an \(240\times 240\) image by a factor of 3, the speed of the original SRCNN[1] is about 1.32 fps, which is far from real-time (24 fps). Experiments also demonstrate this assumption. In this paper, we aim at accelerating the current SRCNN, and propose a compact hourglass-shape CNN structure for faster and better SR. We re-design the SRCNN structure mainly in three aspects. One of its small-size version can run in real-time (>24 fps) on a generic CPU with better restoration quality than SRCNN[1]. blog; statistics; browse. Shrinking: In SRCNN, the mapping step generally follows the feature extraction step, then the high-dimensional LR features are mapped directly to the HR feature space. Restoration: The high resolution maps were then subjected to 3 filters (since the image is composed of 3 channels) of size 5 x 5 in order to aggregate the high resolution mappings in the previous layer. In SRCNN, the filter size of the first layer is set to be 9. In: ICCV, pp. Can biased people create unbiased algorithms? In: ICCV, vol. As we have obtained a well-trained model under the upscaling factor 3 (in Sect. 2018 9th International Conference on Information Technology in Medicine and Education (ITME). Google Scholar, Yang, C.Y., Yang, M.H. [8] further replace the mapping layer by a set of sparse coding sub-networks and propose a sparse coding based network (SCN). The second restriction lies on the costly non-linear mapping step. We take advantage of this property to set the stride \(k=n\), which is the desired upscaling factor. In: CVPR, pp. ECCV 2016. 711730. Before SRCNN came about, a pre-existing method called Sparse Coding was used for image restoration. home. Abstract: As a successful deep model applied in image super-resolution (SR), the Super-Resolution Convolutional Neural Network (SRCNN) has demonstrated superior performance to the previous hand-crafted models either in speed and restoration quality. It involved extraction or cropping out of various patches from an image in an overlapped manner and converting them into a high dimensional vector for further processing. More importantly, the noise, which seriously influences quality, cannot be seen in the resulting images. In: NIPS, pp. (eds.) In the next section, we present the FSRCNN by giving special attention to these two facets. It used a pipeline, which involved extracting overlapping patches from the image, mapping them to a higher resolution space and then aggregating this high resolution vectors and then restore the image by aggregating these vectors. Obviously, with the transferred parameters, the network converges very fast (only a few hours) with the same good performance as that training form scratch. TPAMI 32(6), 11271133 (2010), CrossRef 7, we could observe some jaggies or ringing effects in the results of FSRCNN-s and SRCNN. As deep models generally benefit from big data, studies have found that 91 images are not enough to push a deep model to the best performance. (eds.) The main reasons of high performance have been presented in the above analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, We propose a deep learning method for single image super-resolution (SR). 1 and 2, the desired FSRCNN network should have at most \(8032\times 1.32/24\times 3^2\approx 3976\) parameters. International Conference on Digital Image Processing. Authors in [22] apply \(1\times 1\) layers to save the computational cost. Among them, the Super-Resolution Convolutional Neural Network (SRCNN) [ 1, 2] has drawn considerable attention due to its simple network structure and excellent restoration quality. In: CVPR, pp. For better understanding, we denote a convolution layer as \(Conv(f_i,n_i,c_i)\), and a deconvolution layer as \(DeConv(f_i,n_i,c_i)\), where the variables \(f_i,n_i,c_i\) represent the filter size, the number of filters and the number of channels, respectively. PubMedGoogle Scholar. Feature Extraction: This part is similar to the first part of SRCNN, but different on the input image. Accelerating the Super-Resolution Convolutional Neural Network 393 Fig.1. As the number of convolutional layers increases, the learning depth increases, and the effect of the model also increases. These keywords were added by machine and not by the authors. This paper proposes a highly accurate and fast single-image super-resolution reconstruction (SISR) method by introducing dense skip connections and Inception-ResNet in deep convolutional neural networks. super-resolution. So we also crop \((n-1)\)-pixel borders on the HR sub-images. View 5 excerpts, references methods and background, By clicking accept or continuing to use the site, you agree to the terms outlined in our. In this paper, we aim at accelerating the current SRCNN, and propose a compact hourglass-shape CNN structure for faster and better SR. We re-design the SRCNN structure mainly in three aspects. Single image super-resolution (SR) aims at recovering a high-resolution (HR) image from a given low-resolution (LR) one. To prepare the training data, we first downsample the original training images by the desired scaling factor n to form the LR images. If the network was learned directly from the original LR image, the acceleration would be significant, i.e.,about \(n^2\) times faster. As a successful deep model applied in image super-resolution (SR), the Super-Resolution Convolutional Neural Network (SRCNN) has demonstrated superior performance to the previous hand-crafted models either in speed and restoration quality. Nevertheless, with the guarantee of good performance, it is easier to cooperate with other acceleration methods to get a faster model. Experiments show that the performance of the PReLU-activated networks is more stable, and can be seen as the up-bound of that for the ReLU-activated networks. Accelerating the Super-Resolution Convolutional Neural Network As a successful deep model applied in image super-resolution (SR), the Super-Resolution Convolutional Neural Network (SRCNN) has demonstrated superior performance to the previous hand-crafted models either in speed and restoration quality. Moreover, the FSRCNN still outperforms the previous methods on the PSNR values especially for \(\times \)2 and \(\times \)3. Patch Extraction: 64 filters of size 9 x 9 x 3 were used to perform the first phase which is patch extraction of the solution pipeline. The FSRCNN (48,12,2) contains only 8,832 parameters, then the acceleration compared with SRCNN-Ex is \(57184/8832\times 9=58.3\) times. The fine-tuning is fast, and the performance is as good as training from scratch (see Sect. Following SRCNN and SCN, we use the Set5[15], Set14 [9] and BSD200[25] dataset for testing. Furthermore, we decompose a single wide mapping layer into several layers with a fixed filter size \(3\times 3\). With this strategy, the training converges much earlier than training with the two datasets from the beginning. TensorFlow implementation of Accelerating the Super-Resolution Convolutional Neural Network [1]. Springer, Cham. Then the output is directly the reconstructed HR image. . During testing, we perform the convolution operations once, and upsample an image to different sizes with the corresponding deconvolution layer. 3.3). Further, we present the parameter settings that can achieve real-time performance on a generic CPU while still maintaining good performance. As the deep models for SR contain no fully-connected layers, the approximation of convolution filters will severely impact the performance. \(S_{HR}\) is the size of the HR image. While training its output was set to be its equivalent high resolution image. This implementation replaces the transpose conv2d layer by a sub-pixel layer [2]. Thus the computation complexity of SRCNN grows quadratically with the spatial size of the HR image (not the original LR image). However, as the LR feature dimension d is usually very large, the computation complexity of the mapping step is pretty high. First, FSRCNN adopts the original low-resolution image as input without bicubic interpolation. (1) The proposed 40-layer ESRGCNN uses group convolutions and residual operations to enhance deep and wide correlations of different channels to implement an efficient SR network. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. We augment the data in two ways. First, we fix d,s and examine the influence of m. Obviously, \(m=4\) leads to better results than \(m=2\) and \(m=3\). 818833. ECCV 2014, Part IV. gay chat rooms prophetic numbers meaning; jet tool dealers near me 1, the FSRCNN networks are much faster than contemporary SR models yet achieving superior performance. With moderate number of filters, the network was able to achieve fast processing speeds even on CPUs. Then we only need to determine the filter number \(n_1\). A cascaded super-resolution convolutional neural network (CSRCNN), which takes a single low-resolution image as an input and reconstructs high-resolution images in a progressive way, achieves superior results compared with those of other state-of-the-art methods, particularly with the 8 upsampling factor. For example, the large SRCNN (SRCNN-Ex)[2] has 57,184 parameters, which are six times larger than that for SRCNN (8,032 parameters). The three sensitive variables are just the controlling parameters for the appearance of the hourglass. 391407, 2016. Cost Function: Following SRCNN, we adopt the mean square error (MSE) as the cost function. These LR/HR sub-image pairs are the primary training data. 2016 Springer International Publishing AG, Dong, C., Loy, C.C., Tang, X. We also notice that the FSRCNN achieves slightly lower PSNR than SCN on factor 4. This work proposes an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN) with two extensions: recursive-supervision and skip-connection, which outperforms previous methods by a large margin. Accelerating the Super-Resolution Convolutional Neural Network, $$\begin{aligned} O\{(f_1^2 n_1 + n_1 f_2^2 n_2 + n_2 f_3^2) S_{HR}\}, \end{aligned}$$, \(Conv(5,d,1)-PReLU-Conv(1,s,d)-PReLU-m\times Conv(3,s,s)-PReLU-Conv(1,d,s)-PReLU-DeConv(9,1,d)\), $$\begin{aligned} \begin{array}{rcl} O\{(25d + sd + 9ms^2 + ds+ 81d)S_{LR}\} = O\{(9ms^2 + 2sd + 106d)S_{LR}\}. The chart is based on the Set14 [9] results summarized in Tables3 and 4. To approach real-time, we should accelerate SRCNN for at least 17 times while keeping the previous performance. In: ICCV, pp. (2) The proposed model achieves a speed up of at least \(40\times \) than the SRCNN-Ex[2] while still keeping its exceptional performance. Google Scholar, Sheikh, H.R., Bovik, A.C., De Veciana, G.: An information fidelity criterion for image quality assessment using natural scene statistics. For ReLU and PReLU, we can define a general activation function as \(f(x_i)=max(x_i,0)+a_imin(0,x_i)\), where \(x_i\) is the input signal of the activation f on the i-th channel, and \(a_i\) is the coefficient of the negative part. The mapping is represented as a deep, View 10 excerpts, references background and methods. Another issue affecting the sub-image size is the deconvolution layer. c Springer International Publishing AG 2016 B. Leibe et al. Experiments show that the proposed model, named as Fast Super-Resolution Convolutional Neural Networks (FSRCNN)Footnote 2, achieves a speed-up of more than \(40\times \) with even superior performance than the SRCNN-Ex. Especially, the FSRCNN-s can run in real-time (>24 fps) on a generic CPU. First, we introduce a deconvolution layer at the end of the network, then the mapping is learned directly from the original low-resolution image (without interpolation) to the high-resolution one. Then we crop the LR training images into a set of \(f_{sub}\times f_{sub}\)-pixel sub-images with a stride k. The corresponding HR sub-images (with size \((nf_{sub})^2\)) are also cropped from the ground truth images. The computation complexity of the network can be calculated as follows. 7, more examples can be found on the project page) are also sharper and clearer than other results. Curves and Surfaces 2011. The FSRCNN consists of convolution layers and a deconvolution layer. In: CVPR, pp. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. Computer Vision - ECCV 2016, 2016, Volume 9906. A corresponding transfer strategy is also proposed for fast training and testing across different upscaling factors. 2, pp. Deeper structures have also been explored in[18, 19]. We use a representative upscaling factor \(n=3\). (Eds. Here, we use four narrow layers to replace a single wide layer, thus achieving better results (33.01 dB) with much less parameters. A larger dataset with more training images will be released on the project page. Part of Springer Nature. As we do not have activation functions at the end, the deconvolution filters are initialized by the same way as in SRCNN (i.e.,drawing randomly from a Gaussian distribution with zero mean and standard deviation 0.001). All networks are trained with Set291, a set of images containing 291 natural images. : Low-complexity single-image super-resolution based on nonnegative neighbor embedding. 17901798 (2014), Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. A deconvolution layer is introduced at the end of the network to perform upsampling Second, The non-linear mapping step in SRCNN is replaced by three steps in FSRCNN, namely the shrinking, mapping, and expanding step. Furthermore, all these networks[8, 18, 19] need to process the bicubic-upscaled LR images. We demonstrate this flexibility in this section. Our model also aims at accelerating CNNs but in a different manner. First, we introduce a deconvolution layer at the end of the network, then the mapping is learned directly from the original low-resolution image (without interpolation) to the high-resolution one. The patch extraction and representation part refers to the first layer, which extracts patches from the input and represents each patch as a high-dimensional feature vector. 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Neighbor embedding aims at recovering a high-resolution ( HR ) image from a low-resolution Observe some jaggies or ringing effects in the results of FSRCNN-s and SRCNN are presented the
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