accelerating the super resolution convolutional neural network
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accelerating the super resolution convolutional neural network
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accelerating the super resolution convolutional neural network
pp To approach real-time, we should accelerate SRCNN for at least 17 times while keeping the previous performance. [2] Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional 5(a). Then we follow the literature . The table below shows a few methods of super resolution approaches. The lenna image from the Set14 dataset with an upscaling factor 3. Single Image Super-Resolution Using Deep CNN with Dense Skip Connections and Inception-ResNet When training with the 91-image dataset, the learning rate of the convolution layers is set to be \(10^{-3}\) and that of the deconvolution layer is \(10^{-4}\). PReLU: For the activation function after each convolution layer, we suggest the use of the Parametric Rectified Linear Unit (PReLU)[23] instead of the commonly-used Rectified Linear Unit (ReLU). Then the last reconstruction part aggregates these features to form the final output image. 2022 Springer Nature Switzerland AG. While training its output was set to be its equivalent high resolution image. [1, 2]. It is widely observed that depth is the key factor that affects the performance. [2] show that the mapping accuracy can be substantially improved by adopting a wider mapping layer, but at the cost of the running time. The proposed FSRCNN networks achieve better super-resolution quality than existing methods, and are tens of times faster. Training Dataset. 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. Which were trained on T91-image dataset, and finetuned on General100 dataset. As we remove some redundant parameters, the network is trained more efficiently and achieves another 0.05 dB improvement. The first layer can be expressed as: Non Linear Mapping: The previously found feature maps were then put through a. gay chat rooms prophetic numbers meaning; jet tool dealers near me The overall shape of the new structure looks like an hourglass, which is symmetrical on the whole, thick at the ends and thin in the middle. This figure shows the network structures of the SRCNN and FSRCNN. LNCS, vol. For the second problem, we add a shrinking and an expanding layer at the beginning and the end of the mapping layer separately to restrict mapping in a low-dimensional feature space. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. Therefore, we can adopt a smaller filter size \(f_1=5\) with little information loss. All parameters are optimized using stochastic gradient descent with the standard backpropagation. However, as it strictly mimics the sparse-coding solver, it is very hard to shrink the sparse coding sub-network with no loss of mapping accuracy. This work proposes a deep learning method for single image super-resolution (SR) that directly learns an end-to-end mapping between the low/high-resolution images and shows that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. However, the high computational cost still hinders it from practical usage that demands real-time performance (24 fps). First, as a pre-processing step, the original LR image needs to be upsampled to the desired size using bicubic interpolation to form the input. As we train our models with the Caffe package[27], its deconvolution filters will generate the output with size \((nf_{sub}-n+1)^2\) instead of \((nf_{sub})^2\). networks. Accelerating the super-resolution convolutional neural network. This Electronic supplementary material The online version of this chapter (doi:10. This makes the resulting image much higher quality. ACCV 2014. : Deeply-recursive convolutional network for image super-resolution. Then according to Eqs. During training, we only fine-tune the deconvolution layer on the 91-image and General-100 datasets of \(\times \)2. : Low-complexity single-image super-resolution based on nonnegative neighbor embedding. This implementation replaces the transpose conv2d layer by a sub-pixel layer [2]. 1). 2016 Springer International Publishing AG, Dong, C., Loy, C.C., Tang, X. where \(\{f_{i}\}_{i=1}^3\) and \(\{n_{i}\}_{i=1}^3\) are the filter size and filter number of the three layers, respectively. where \(Y_s^i\) and \(X^i\) are the i-th LR and HR sub-image pair in the training data, and \(F(Y_s^{i} ; \theta )\) is the network output for \(Y_s^i\) with parameters \(\theta \). 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) [1 , 2 ] has demonstrated superior performance to the previous hand-crafted models either in speed and restoration quality. As shown in Fig. In: CVPR, pp. If we force these kernels to be identical, the parameters will be used inefficiently (equal to sum up the input feature maps as one), and the performance will drop at least 0.9 dB on the Set5. They are all of good quality with clear edges but fewer smooth regions (e.g.,sky and ocean), thus are very suitable for the SR training. With the collaboration of a set of deconvolution filters, the network can learn an end-to-end mapping between the original LR and HR images with no pre-processing. (more information) . A novel Progressive Residual Network (PRNet) is proposed to integrate hierarchical and scale features for single image SR, which works well for both small and large scaling factors. Then during fine-tuning, the learning rate of all layers is reduced by half. 111126. As indicated in SRCNN[2], a \(5\times 5\) layer achieves much better results than a \(1\times 1\) layer. 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. View 2 excerpts, references methods and background, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). We choose PReLU mainly to avoid the dead features[11] caused by zero gradients in ReLU. In: CVPR, pp. 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. The input to the model was a standard low resolution image. Convergence curves for different training strategies. Specifically, we find that all convolution layers on the whole act like a complex feature extractor of the LR image, and only the last deconvolution layer contains the information of the upscaling factor. Further, we present the parameter settings that can achieve real-time performance on a generic CPU while still maintaining good performance. As they are written in different programming languages, the comparison of their test time may not be fair, but still reflects the main trend. Contrarily, if we exchange the position of the input and output, the output will be k times of the input, as depicted in Fig. The proposed FSRCNN networks achieve better super-resolution quality than existing methods, and are tens of times faster. [8] further replace the mapping layer by a set of sparse coding sub-networks and propose a sparse coding based network (SCN). The convolution layers can be shared for different upscaling factors. Third, we adopt smaller filter sizes but more mapping layers. 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. 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. Obviously, with the transferred parameters, the network converges very fast (only a few hours) with the same good performance as that training form scratch. 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. Experiments show that this hourglass design is very effective for image super-resolution. In: ECCV (2016), Hui, T.W., Loy, C.C., Tang, X.: Depth map super resolution by deep multi-scale guidance. As shown in Fig. Accelerating the Super-Resolution Convolutional Neural Network Chao Dong, Chen Change Loy, Xiaoou Tang 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. 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 computation complexity of the network can be calculated as follows. Compared to the existing SR networks, DSCNN has two advantages: (1) it can handle the super-resolution images with arbitrary scale factors, and (2) it dynamically predicts the values rather than the weights of the interpolated pixels of HR images. Therefore, we contribute a new General-100 dataset that contains 100 bmp-format images (with no compression)Footnote 4. 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. First, we fix d,s and examine the influence of m. Obviously, \(m=4\) leads to better results than \(m=2\) and \(m=3\). ECCV 2014, Part IV. Convergence curves of different network designs. Training Samples. They are different on the coefficient of the negative part. Before SRCNN came about, a pre-existing method called Sparse Coding was used for image restoration. Xiaoou Tang, . However, the high computational cost still hinders it from practical usage that demands real-time . Then, to maintain the same good performance as SRCNN, we use multiple \(3\times 3\) layers to replace a single wide one. As a successful deep model applied in image super-resolution (SR), the Super-Resolution Convolutional Neural Network (SRCNN) [1 , 2 ] has demonstrated superior performance to the previous hand-crafted models either in speed and restoration quality. This paper aims to accelerate the test-time computation of convolutional neural networks, especially very deep CNNs, and develops an effective solution to the resulting nonlinear optimization problem without the need of stochastic gradient descent (SGD). It is worth noting that this acceleration is NOT at the cost of performance degradation. If we need to apply several upscaling factors simultaneously, this property can lead to much faster testing (as illustrated in Fig. This work is partially supported by SenseTime Group Limited. We first look at the test time, which is the main focus of our work. Second, we reformulate the mapping layer by shrinking the input feature dimension before mapping and expanding back afterwards. [24] and Schulteret al. There are two key characteristics of these attacks: firstly, these perturbations are mostly additive noises carefully crafted from the deep neural network itself. blog; statistics; browse. blog; statistics; browse. In: NIPS, pp. Curves and Surfaces 2011. 561568 (2013), Timofte, R., De Smet, V., Van Gool, L.: Anchored neighborhood regression for fast example-based super-resolution. 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. These keywords were added by machine and not by the authors. 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). ICCV (2015) 370378. We present a fully convolutional neural network for image super-resolution. 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. Figure 7: Benchmark table for different super-resolution approaches. super-resolution. 4). Springer, Heidelberg (2014), Xu, L., Ren, J.S., Liu, C., Jia, J.: Deep convolutional neural network for image deconvolution. This process is experimental and the keywords may be updated as the learning algorithm improves. Then the non-linear mapping part can be represented as \(m\times Conv(3, s, s)\). In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds) Computer Vision ECCV 2016. This tutorial describes one way to implement a CNN (convolutional neural network) for single image super-resolution optimized on Intel architecture from the Caffe* deep learning framework and Intel Distribution for Python*, which will let us take advantage of Intel processors and Intel libraries to accelerate training and testing of this CNN.. 8689, pp. Third, we adopt smaller lter sizes but more mapping layers. This strategy greatly reduces the number of parameters (detailed computation in Sect. Therefore, we can highly praise the productivity of the convolutional neural network . Deeper structures have also been explored in[18, 19]. 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. 8693, pp. Proceedings of the European conference on computer vision, Springer (2016) 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. Bibliographic details on Accelerating the Super-Resolution Convolutional Neural Network. However, images in the BSD500 are in JPEG format, which are not optimal for the SR task. Though SRCNN is already faster than most previous learning-based methods, the processing speed on large images is still unsatisfactory. 370378 (2015), Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. in order to quickly and effectively realize superresolution models at different scales, dong et al. Second, we reformulate the mapping layer by shrinking the input feature dimension before mapping and expanding back afterwards. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Our model also aims at accelerating CNNs but in a different manner. Moreover, the FSRCNN still outperforms the previous methods on the PSNR values especially for \(\times \)2 and \(\times \)3. We note that it carries very different meaning in classic image processing, see[12]. The results of PSNR (dB) and test time (sec on CPU) on three test datasets. Similarly, if we reverse back, the deconvolution filters should also have a spatial size \(f_5=9\). We also show their performance (average PSNR on Set5) trained on the 91-image dataset[10]. 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}\}. For the issue of padding, we empirically find that padding the input or output maps does little effect on the final performance. Furthermore, we decompose a single wide mapping layer into several layers with a fixed filter size \(3\times 3\). 19201927 (2013), Timofte, R., DeSmet, V., VanGool, L.: A+: adjusted anchored neighborhood regression for fast super-resolution. Now we want to find a more concise FSRCNN network that could realize real-time SR while still keep good performance. The proposed model achieves a speed up of more than 40 times with even superior restoration quality. Another advantage of FSRCNN over the previous learning-based methods is that FSRCNN could achieve fast training and testing across different upscaling factors. This is mainly because that the SCN adopts two models of \(\times \)2 to upsample an image by \(\times \)4. More importantly, the noise, which seriously influences quality, cannot be seen in the resulting images. View 6 excerpts, cites methods and background. To maintain consistency with the shrinking layer, we also adopt \(1\times 1\) filters, the number of which is the same as that for the LR feature extraction layer. Yanget al. This trend can also be observed from the convergence curves shown in Fig. However, as we pay more attention to speed, we still present the results of a single network. Furthermore, all these networks[8, 18, 19] need to process the bicubic-upscaled LR images. persons; conferences . Figure 8 visualizes the performance of state of the art . 10261034 (2015), Yang, C.-Y., Ma, C., Yang, M.-H.: Single-Image super-resolution: a benchmark. 12691277 (2014), Zhang, X., Zou, J., He, K., Sun, J.: Accelerating very deep convolutional networks for classification and detection. 818833. ECCV 2014, Part IV. Based on the same assumption, Wanget al. Google Summer of Code with OpenCV Google Scholar, Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. While observing the limitations of current deep learning based SR models, we explore a more efficient network structure to achieve high running speed without the loss of restoration quality. To be specific, the parameters of all convolution filters in the well-trained model are transferred to the network of \(\times \)2. However, the high computational cost still hinders it from practical usage that demands real-time performance (24 fps). 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. CoRR abs/1608.00367 (2016) a service of . Secondly, the noises are added. (Eds. (2014) 184199. The proposed FSCNN and FSRCNN-s are trained on both 91-image and General-100 dataset. As the learned deconvolution kernels are better than a single bicubic kernel, the performance increases roughly by 0.12 dB. In addition, we also explore a two-step training strategy. By adopting a smaller filter number \(n_2=s<24 fps) on a generic CPU. In: ICCV, pp. (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. DOI: 10.1007/978-3-319-46475-6 25 392 C. Dong et al. Extracting high resolution images from low resolution images is a classical problem in computer vision. FSRCNN SRCNN . As we have obtained a well-trained model under the upscaling factor 3 (in Sect. 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. The learned deconvolution layer (56 channels) for the upscaling factor 3. Then we will have \(5\times 4-1=19\) times more images for training. Deconvolution: The last part is a deconvolution layer, which upsamples and aggregates the previous features with a set of deconvolution filters. Second, we fix m and examine the influence of d and s. In general, a better result usually requires more parameters (e.g.,a larger d or s), but more parameters do not always guarantee a better result. Different from the conventional learning-based methods, SRCNN directly learns an end-to-end mapping between LR and HR images, leading to a fast and accurate inference. The example of license plate detection using an iPhone. [21] make attempts to accelerate very deep CNNs for image classfication. The non-linear mapping part refers to the middle layer, which maps the feature vectors non-linearly to another set of feature vectors, or namely HR features. In: CVPR, pp. First, we calculate how many parameters can meet the minimum requirement of real-time implementation (24 fps). This work presents a highly accurate single-image superresolution (SR) method using a very deep convolutional network inspired by VGG-net used for ImageNet classification and uses extremely high learning rates enabled by adjustable gradient clipping. 1 Introduction Single image super-resolution (SR) aims at recovering a high-resolution (HR) image from a given low-resolution (LR) one. We exclude the parameters of PReLU, which introduce negligible computational cost. In SRCNN, the filter size of the first layer is set to be 9. Lastly, we can represent the deconvolution layer as DeConv(9,1,d). home. 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. If the network was learned directly from the original LR image, the acceleration would be significant, i.e.,about \(n^2\) times faster. 184199. 416423 (2001), Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. ECCV 2014, Part I. LNCS, vol. We are hiring! Springer, Heidelberg (2012), Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. Contrarily, the FSRCNN (56,12,4) outperforms SRCNN-Ex by a large margin (e.g.,0.23 dB on the Set5 dataset). Our method directly learns an end-to-end mapping between the low/high-resolution images. This phenomenon is also observed in some deep models for high-level vision tasks. As opposed to the shrinking layer Conv(1,s,d), the expanding layer is Conv(1,d,s). As mentioned above it aggregated the solving technique of complex algorithmic pipelines used earlier into a single convolutional network thus providing an end to end solution for the problem. International Conference on Digital Image Processing. The SRCNN paper published in 2015 was a major improvement to the pre-existing solutions for this problem since it was simple, efficient and provided an end to end solution. Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong, China, You can also search for this author in TensorFlow implementation of Accelerating the Super-Resolution Convolutional Neural Network [1]. (2016). To approach real-time, we should accelerate SRCNN for at least 17 times while keeping the previous performance. View 17 excerpts, references methods and background. We have also done comprehensive comparisons with more SR algorithms in terms of PSNR, SSIM and IFC[29], which can be found in the supplementary file. The computational complexity can be calculated as. . Overall, there are 5 more layers, but the parameters are decreased from 58,976 to 17,088. Motivated by SRCNN, some problems such as face hallucination[16] and depth map super-resolution[17] have achieved state-of-the-art results. During testing, we only need to do convolution operations once, and upsample an image to different scales using the corresponding deconvolution layer. Thus we assign a reasonable value to the insensitive variables in advance, and leave the sensitive variables unset. Super Resolution Convolutional Neural Network- An Intuitive Guide Extracting high resolution images from low resolution images is a classical problem in computer vision. Accelerating the Super-Resolution Convolutional Neural Network. In Fig. We present the best results reported in the corresponding paper. We use a representative upscaling factor \(n=3\). This paper shows that a sparse coding model particularly designed for super-resolution can be incarnated as a neural network, and trained in a cascaded structure from end to end, and leads to much more efficient and effective training, as well as a reduced model size.
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