5. /R47 29 0 R In addition, it also can be seen that our SRFBN+ outperforms almost all comparative methods. -11.9547 -11.9551 Td propose an image super-resolution feedback network (SRFBN) to refine low-level /XObject << T* The FB at the t-th iteration receives the hidden state from previous iteration Ft1out through a feedback connection and shallow features Ftin. Can Neural Networks Understand Logical Entailment? /R17 76 0 R >> As aforementioned, the proposed SRFBN is trained using curriculum learning strategy for BD and DN degradation models, and fine-tuned based on BI degradation model using DIV2K. /F1 114 0 R /R11 11.9552 Tf To dig deeper into the difference between feedback and feedforward networks, we visualize the average feature map of every iteration in SRFBN-L and SRFBN-L-FF, illustrated in Fig. >> SRFBNR. /R41 22 0 R W The comprehensive experimental results have demonstrated that the proposed SRFBN could deliver the comparative or better performance in comparison with the state-of-the-art methods by using very fewer parameters. 71.934 4.33906 Td /R8 46 0 R /Font << 4437.59 5261.21 m 5030.75 5554.68 m h where Cg refers to the downsample operation using Conv(k,m) at the g-th projection group. Single Image Super-Resolution (SISR) is the reconstruction of a given single low-resolution image into a corresponding high-resolution image. BT >> [ (g) -0.29866 ] TJ /R86 122 0 R [ (herently) -483 (ill\055posed) -482.982 (since) -483.008 (multiple) -483.99 (HR) -481.982 (images) -484.015 (may) -483.005 (result) ] TJ 10 0 0 10 0 0 cm >> 3615.35 5010.34 3628.57 5023.56 3628.57 5039.88 c S /R11 65 0 R [ (t) -0.80051 ] TJ (I1HR,I2HR,,ITHR) are identical for the single degradation model. /F1 101 0 R Specifically, we use hidden states in an RNN with constraints to achieve such feedback manner. Now Electrek has learned that Tesla has. This paper proposes residual dense block (RDB) to extract abundant local features via dense connected convolutional layers and uses global feature fusion in RDB to jointly and adaptively learn global hierarchical features in a holistic way. A feedback block is [ (tions\056) -478.005 (The) -306.012 (pr) 44.9839 (oposed) -304.986 (SRFBN) -305.997 (comes) -305.983 (with) -305.996 (a) -305.983 (str) 44.9925 (ong) -306.013 (early) -306.018 (r) 37.0183 (e\055) ] TJ Imagery, Gated Multiple Feedback Network for Image Super-Resolution, Feedback Pyramid Attention Networks for Single Image Super-Resolution, Deep learning architectural designs for super-resolution of noisy images, Dual Reconstruction Nets for Image Super-Resolution with Gradient 2022 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). The initial state F0out is set to F1in, hence the first iteration in the proposed network cant receive the feedback information. [ (and) -326.022 (hidden) ] TJ /R238 280 0 R Correspondingly, Ltg can be obtained by. Specifically, we use hidden states in an RNN with constraints to achieve such feedback manner. The settings of input patch size are listed in Tab. /R251 320 0 R We also observe that fine-tuning on a network pretrained on the BI degradation model leads to higher PSNR values than training from scratch. /a1 gs s1mpleZzz. Such large-capacity networks occupy huge amount of storage resources and suffer from overfitting. Seven ways to improve example-based single image super resolution. Y.Bengio, J.Louradour, R.Collobert, and J.Weston. T* /R229 278 0 R ET 0 Tc (\056) Tj f* Obviously, our proposed SRFBN can outperform almost all comparative methods. The global residual skip connection at each iteration. 1 0 0 rg and thus is more suitable for image SR tasks. 5.88398 -0.99609 Td 5, The proposed SRFBN and SRFBN+ achieve the best on almost all quantative results over other state-of-the-art methods. /R9 62 0 R [ (et) -229.998 (al) ] TJ Look and think twice: Capturing top-down visual attention with A lightweight network SRFBN-S (T=4, G=3, m=32) is provided to compare with the state-of-the-art methods, which are carried only few parameters. >> 100.875 18.547 l An Edge-enhanced with Feedback Attention Network for image super-resolution (EFANSR) is proposed, which comprises three parts and introduces feedback mechanism to feed high-level information back to the input and fine-tune the input in the dense spatial and channel attention block. Based on back-projection, Haris et al. 4.73203 -4.33789 Td [ (Sichuan) -250.012 (Uni) 24.9946 (v) 14.9862 (ersity) 64.9887 (\054) ] TJ S.Schulter, C.Leistner, and H.Bischof. 4929.54 5408.58 m T* 4.1, we now present our results for two experiments on two different degradation models, i.e. The mathematical formulation of the reconstruction block is: where fRB denotes the operations of the reconstruction block. Q Super-Resolution Generative Adversarial Network (SRGAN) - Uses the idea of GAN for super-resolution task i.e. 9. After adding DSC to the FB, the reconstruction performance can be further improved, because the information efficiently flows through DSC across hierarchy layers and even across time. /R8 46 0 R [ (F) -0.19604 ] TJ T* /R56 8.9664 Tf 10 0 0 10 0 0 cm /R69 102 0 R Q >> << /R123 217 0 R In this paper, we propose an image super-resolution feedback network (SRFBN) to refine low-level representations with high-level information. 79.777 22.742 l q /Annots [ ] Sketch-based manga retrieval using manga109 dataset. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). [ (2) -0.29866 ] TJ /R55 Do W.Han, S.Chang, D.Liu, M.Yu, M.Witbrock, and T.S. Huang. q /R8 46 0 R /R9 11.9552 Tf >> /R93 164 0 R This further demonstrates the powerful representation ability of our proposed FB. Single image super-resolution with non-local means and steering This comparison shows the effectiveness of the proposed SRFBN. /R44 19 0 R T* The reconstruction block uses Deconv(k,m) to upscale LR features Ftout to HR ones and Conv(3,cout) to generate a residual image ItRes. (in) Tj 0 g /Font << 2017 IEEE International Conference on Computer Vision (ICCV). 83.789 8.402 l /Annots [ ] From Fig. 3 0 obj mechanism, which commonly exists in human visual system, has not been fully 48.406 3.066 515.188 33.723 re /R11 65 0 R To address this problem, we propose a new SISR algorithm called a Deep Residual Squeeze and Excitation Network (DRSEN). << 3463.92 4959.46 m /R254 315 0 R The results of D-DBPN are cited from their supplementary materials. /R27 9.02448 Tf . Besides, some face super-resolution networks do not consider the mutual promotion . 3421.09 5261.21 m Fast and accurate image upscaling with super-resolution forests. First, compared with the feedforward network at early iterations, feature maps acquired from the feedback network contain more negative values, showing a stronger effect of suppressing the smooth area of the input image, which further leads to a more accurate residual image. Zoom Camera Module . (1) Tj << From Fig. /MediaBox [ 0 0 612 792 ] /R274 325 0 R 48.8602 0 Td It is worth noticing that small T and G still outperform VDSR[18]. Feedback Network for Image Super-ResolutionCVPR-20191. /Rotate 0 arXiv preprint arXiv:1902.06068. paper. Conclusions DenseED blocks in neural networks show accurate extraction of super-resolution images even if the ML model is trained with a small training dataset of 15 field-of-views. Image super-resolution via deep recursive residual network. connections. /R45 18 0 R /MediaBox [ 0 0 612 792 ] Abstract The image super-resolution algorithm based on deep learning has a good reconstruction effect, and the reconstruction can be further enhanced by using multi-scale features. For BI degradation model, we compare the SRFBN and SRFBN+ with seven state-of-the-art image SR methods: SRCNN[7], VDSR[18], DRRN[31], SRDenseNet[36], MemNet[36], EDSR[23], D-DBPN[11]. [18] increased the depth of CNN to 20 layers for more contextual information usage in LR images. Because the large memory consumption in Caffe, we re-implement MemNet on Pytorch for fair comparison. The rapid development of deep learning (DL) has driven single image super-resolution (SR) into a new era. -71.202 -37.8582 Td [ (art) -389.995 (methods\056) -730.986 (Code) -389.995 (is) -390 (avaliable) -389.98 (at) ] TJ Kim et al. /R161 172 0 R /Annots [ ] 3.98 w [ (1) -0.30019 ] TJ xY[_q~mdi,,89 `zoj$oN>n_mf{sx~bnz{AGY-/=84~L(\Yv7TwH+^8DI{T|0xkw3O1K\a_0()"S2dQF=>L"\5R@4]vpx&)D;h}4Z?^fPX)P3NfD;gQ0@rUT(\gh-VM0= 2\l/6i:jqj|Z)/=vZ'bXxW#v#And=.QyPc1$P >> Image super-resolution using very deep residual channel attention f* The results are shown in Tab. q [ (the) -354.992 (pr) 44.9839 (oposed) -353.997 (SRFBN) -355.008 (in) -354.007 (comparison) -354.996 (with) -353.987 (the) -354.992 (state\055of\055the\055) ] TJ /ExtGState << /ExtGState << 109.984 9.465 l https://github.com/Paper99/SRFBN_CVPR19. /R21 5.9776 Tf In effect this makes it easy to cause denial-of. /R13 7.9701 Tf /Author (Zhen Li\054 Jinglei Yang\054 Zheng Liu\054 Xiaomin Yang\054 Gwanggil Jeon\054 Wei Wu) /Rotate 0 4.4. as the activation function following all convolutional and deconvolutional layers except the last layer in each sub-network. >> For each LR image, its target HR images for consecutive iterations are arranged from easy to hard based on the recovery difficulty. 3628.57 5096.37 3615.35 5109.59 3599.03 5109.59 c [8], utilized curriculum learning to solve the fixation problem in image restoration. In other words, our feedback block surely benefits the information flow across time. endstream 11.9551 TL Q Feedback Network for Image Super-ResolutionCVPR-2019 1. J.Carreira, P.Agrawal, K.Fragkiadaki, and J.Malik. On single image scale-up using sparse-representations. A.Pentina, V.Sharmanska, and C.H. Lampert. /R230 288 0 R 78.852 27.625 80.355 27.223 81.691 26.508 c >> >> 5047.71 5558.89 l representations. /Annots [ ] /R81 115 0 R In addition, F1in are regarded as the initial hidden state F0out. Densely connected convolutional networks. /R237 281 0 R << High-level information is provided in top-down feedback flows through feedback connections. Noticeably, our proposed FB obtains the best quantitative results in comparison with other basic blocks. /Subject (IEEE Conference on Computer Vision and Pattern Recognition) /R86 122 0 R 1 0 obj The state-of-the-art methods considered in this experiment include SRCNN[7], VDSR[18], DRRN[31], MemNet[36], EDSR[23], DBPN-S[11] and D-DBPN[11]. ET Super-resolutionMulti-exposure Image FusionCoupled Feedback NetworkCF-Net . C.Schroers. W.Xu, D.Ramanan, and T.S. Huang. The SRFBN-S can achieve the best SR results among the networks with parameters fewer than 1000K. 10 0 0 10 0 0 cm [ (le) 14.981 (vel) -351.001 (r) 37.0196 (epr) 36.981 (es) 0.98207 (entations) -350.997 (with) -351.006 (high\055le) 15 (vel) -349.982 (information\056) -611.989 <5370656369022d> ] TJ [ (Zhen) -249.992 (Li) ] TJ [ (super) 20.0144 (\055r) 37.0165 (esolution) -323.981 (feedbac) 20.0052 (k) -323.01 (network) -323.996 (\050SRFBN\051) -322.98 (to) -324.012 (r) 37.0183 <65026e65> -323.983 (low\055) ] TJ >> T* Single image super-resolution has high research value and also has important applications in the fields of surveillance equipment, satellite imagery, and medical imaging. As shown in Fig. /Contents 167 0 R 3959.21 5129.67 l Degradation models. The choices of networks for comparison include D-DBPN (which is a state-of-the-art network with moderate parameters) and MemNet[32] (which is the leading network with recurrent structure). The structure of LPFN is shown in Fig. By Anil Chandra Naidu Matcha. The Principle of SRFBN. T* /Type /Page /R23 87 0 R background by v1, v2 and v3 neurons. /R62 45 0 R However, the feedback [ (F) 15.9919 (e) -11.9779 (e) -11.9779 (dba) 31.0156 (c) -11.9779 (k) ] TJ Thus, we explore the design of the basic block in this section. /R15 7.9701 Tf Our SRFBN-S (T=4, G=3, m=32) and final SRFBN (T=4, G=6, m=64) are provided for this comparison. 2 0 obj 5047.71 5408.58 l -0.00654 Tc 200 epochs are trained with batch size of 16. /R260 326 0 R /R11 11.9552 Tf To use consecutive contexts within a low-resolution sequence, VSR learns the spatial and temporal characteristics of multiple frames of the low-resolution sequence. /R17 9.9626 Tf 4389.74 5081.83 l /Rotate 0 our network consists of two main structures: (1) recursive inference block based on dense connection reuse of local low-level features, and recursive learning is applied to control the model parameters while increasing the receptive fields; (2) a bidirectional convolutional lstm (biconvlstm) layer is introduced to learn the correlations of In the following discussions, we use SRFBN-L (T=4, G=6) for analysis. 10 0 0 10 0 0 cm training from scratch and fine-tuning on a network pretrained on the BI degradation model. In order to make the hidden state in SRFBN carry a notion of output, we tie the loss for every iteration. 1 Highly Influenced PDF We use a bilinear upsample kernel here. 3333.19 5039.88 l Valve offers a Steam Deck recovery image that will get SteamOS back in working order provided you're OK with. /R162 168 0 R /R17 76 0 R To fully exploit contextual information from LR images, we feed RGB image patches with different patch size based on the upscaling factor. /Type /Page ET S [ (nism\054) -351.996 (whic) 14.9987 (h) -332.018 (commonly) -332.003 (e) 19.9918 (xists) -330.982 (in) -332.011 (human) -332 (visual) -331.996 (system\054) -352.003 (has) ] TJ 07:34. /Parent 1 0 R T* 71.715 5.789 67.215 10.68 67.215 16.707 c /R47 29 0 R /Resources << /R50 32 0 R In this paper, we propose an image super-resolution feedback network (SRFBN) to refine low-level representations with high-level information. Y.Wang, F.Perazzi, B.Mcwilliams, A.Sorkinehornung, O.Sorkinehornung, and T* l. Furthermore, we design a curriculum for the case, in which the LR image is generated by a complex degradation model. /R173 231 0 R /R30 4.74493 Tf /a1 gs In this paper, we propose an image super-resolution feedback network (SRFBN) to refine low-level representations with high-level information. [ (\050RNN\051) -371.992 (with) -371.004 (constr) 15.0024 (aints) -371.986 (to) -372.004 (ac) 15.0183 (hie) 14.9852 (ve) -371.005 (suc) 14.9852 (h) -372.011 (feedbac) 20.0065 (k) -371.982 (manner) 110.981 (\056) ] TJ /R9 62 0 R 0.76953 0.83984 0.62695 rg Any absence of these three parts will fail the network to drive the feedback flow. Deep learning for image super-resolution: A survey. ; The proposed SRFBN comes with a strong early . The research in our paper is sponsored by National Natural Science Foundation of China (No.61701327 and No.61711540303), Science Foundation of Sichuan Science and Technology Department (No.2018GZ0178). The feedback mechanism in these architectures works in a top-down manner, carrying high-level information back to previous layers and refining low-level encoded information. 8 0 obj To keep consistency with previous works, quantitative results are only evaluated on luminance (Y) channel. /R42 21 0 R 0. (Abstract) Tj 5047.71 5507.8 m /R46 17 0 R /R13 7.9701 Tf (F) Tj f* task. 4. are re-evaluated from the corresponding public codes. 4 0 obj << Experimental results demonstrate the superiority of our proposed SRFBN against other state-of-the-art methods. An Edge-enhanced with Feedback Attention Network for image super-resolution (EFANSR) is proposed, which comprises three parts and introduces feedback mechanism to feed high-level information back to the input and fine-tune the input in the dense spatial and channel attention block. 5169.88 5237.93 l A self-ensemble method[35] is also used to further improve the performance of the SRFBN (denoted as SRFBN+). Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Particularly, the recurrent structure plays an important role to realize the feedback process in the proposed SRFBN (see Fig. endobj <0010> Tj Download this share file about Feedback Neural Network based Super-resolution of DEM for generating high fidelity features from Eduzhai's vast library of public domain share files. /Contents 106 0 R /R13 7.9701 Tf /R60 5.9776 Tf The learning rate multiplies by 0.5 for every 200 epochs. 2, our proposed SRFBN can be unfolded to T iterations, in which each iteration t is temporally ordered from 1 to T. . [ (performance\056) -400.009 (The) -279.988 <62656e65027473> -280.002 (of) -279.992 (deep) -281.012 (learning) -280.012 (based) -279.983 (methods) ] TJ Enhanced deep residual networks for single image super-resolution. /R177 225 0 R T* /R243 291 0 R >> /R11 11.9552 Tf /ExtGState << /R58 5.9776 Tf q XGODY Kids Tablet Android 11.0 2GB 32GB 7 Inch HD Screen Children Learning Tablet PC Quad Core 1024x600 WiFi Dual Camera Tablets CPU: Cortex-A133 quad-core, 1.5GHz basic frequency/ULP processor GPU: Mali- G31MP2 graph System: Android 11.0, 2GB RAM+32GB ROM Camera: Built-in Dual Camera, Front 0.3MP + Rear 2.0MP Battery: 3.7V/3000mAh WIFI: Wi-Fi 802.11 b/g/n Bluetooth: Support SD (TF) card . Meanwhile, such recurrent structure with feedback connections provides strong early reconstruction ability, and requires only few parameters. [ (29\054) -249.985 (6\054) -249.99 (18\135\056) ] TJ On one hand, the low-level features can be refined by hight-level ones in each feedback procedure. /R17 76 0 R Fig. Google Scholar Cross Ref; Juncheng Li, Faming Fang, Kangfu Mei, and Guixu Zhang. DRCN[19] and DRRN[31]. ) 11.9547 TL 96.422 5.812 m Q 11.9563 TL 3895.18 5261.21 l /Type /Page /R187 236 0 R 1(b)). /R34 27 0 R [ (posed) -313.992 (netw) 10 (ork\056) -504 (Blue) -315.019 (arro) 25.0038 (ws) -313.981 (represent) -314.003 (the) -314.989 (feedback) -313.981 (connections\056) ] TJ endobj /Resources << /MediaBox [ 0 0 612 792 ] The proposed SRFBN is essentially an RNN with a feedback block (FB), which is specifically designed for image SR tasks. 5064.67 5554.68 l /Producer (PyPDF2) More effective basic block could generate finer high-level representations and then benefits our feedback process. Motivated by this phenomenon, recent studies[30, 40] have applied the feedback mechanism to network architectures. Enhancing the resolution of underwater images leads to better performance of autonomous underwater vehicles. We formulate the curriculum based on the recovery difficulty. [ (methods\13342\135\054) -354.017 (and) -334.017 (learning\055based) -332.981 (methods) -333.986 (\13333\054) -334.013 (26\054) -332.991 (34\054) -334.01 (15\054) ] TJ S >> T* Therefore, this paper proposes a self-attention negative feedback network (SRAFBN) for realizing the real-time image SR. Residual dense network for image super-resolution. /MediaBox [ 0 0 612 792 ] On-demand learning for deep image restoration. /R19 84 0 R The results shown in Tab. Proposing a feedback block (FB), which not only efficiently handles feedback information flows, but also enriches high-level representations via up- and down-sampling layers, and dense skip connections. [ (4) -0.30019 ] TJ /R110 138 0 R This work proposes a deep Iterative Super-Resolution Residual Convolutional Network (ISRResCNet) that exploits the powerful image regularization and large-scale optimization techniques by training the deep network in an iterative manner with a residual learning approach. /F2 336 0 R from publication: Gated Multi . /R178 224 0 R /ExtGState << /Parent 1 0 R T* /R164 212 0 R /R199 263 0 R Q /F2 270 0 R 96.449 27.707 l 6, when UDSL is replaced with 33 sized convolutional layers in the FB, the PSNR value dramatically decreases. q imagenet classification. This further indicates that initial low-level features, which lack enough contextual information, surely are corrected using high-level information through the feedback mechanism in the proposed network. Curriculum learning[2], which gradually increases the difficulty of the learned target, is well known as an efficient strategy to improve the training procedure. 5.75625 0 Td /R164 212 0 R [11] designed up- and down-projection units to achieve iterative error feedback. /Parent 1 0 R In the feedforward network, feature maps vary significantly from the first iteration (t=1) to the last iteration (t=4): the edges and contours are outlined at early iterations and then the smooth areas of the original image are suppressed at latter iterations. 10 0 0 10 0 0 cm /R69 102 0 R [ (for) -371.998 (mor) 36.9883 (e) -371.001 (complicated) -371.984 (tasks\054) -401.989 (wher) 36.9938 (e) -371.002 (the) -372.009 (low\055r) 37.0036 (esolution) -372.011 (im\055) ] TJ /Annots [ ] f 3) which form our feedback system. /F1 159 0 R For complex degradation models, (I1HR,I2HR,,ITHR) are ordered based on the difficulty of tasks for T iterations to enforce a curriculum. 78.598 10.082 79.828 10.555 80.832 11.348 c [ (1) -0.30019 ] TJ /Parent 1 0 R (\050a\051) Tj q 2Mp 90x Optical Zoom and 640512 Thermal Bi-spectrum Heavy Load High Precision Network PTZ Camera - Savgood Detail: . /Parent 1 0 R IEEE Transactions on Pattern Analysis and Machine Intelligence, We propose a deep learning method for single image super-resolution (SR). Adam: A method for stochastic optimization. /Type /Page >> In this paper, we 3540.88 5099.63 114.324 120.297 re At present, most of face super-resolution (SR) networks cannot balance the visual quality and the pixel accuracy. /R60 96 0 R Our method directly learns an end-to-end mapping between the low/high-resolution images. /ProcSet [ /Text /ImageC /ImageB /PDF /ImageI ] Delving deep into rectifiers: Surpassing human-level performance on Recent studies[22, 10] have shown that many networks with recurrent structure (e.g. /Type /Page D.R. Martin, C.C. Fowlkes, D.Tal, and J.Malik. In the context of image SR, Wang. S.Savarese. q Recent advances in image super-resolution (SR) explored the power of deep learning to achieve a better reconstruction performance. Deep learning has shown its superior performance in various computer vision tasks including image SR. Dong et al. can be extrapolated as a single-state Recurrent Neural Network (RNN). Q /CA 1 [ (Zheng) -250.004 (Liu) ] TJ VDSR, EDSR and D-DBPN fail to recover the clear image. q /R19 9.9626 Tf To some extent, this illustration reflects the reason why the feedback network has more powerful early reconstruction ability than the feedforward one. /Annots [ ] and filters in each basic block is set to 12 and 32, respectively. It has standard wear on the outer metal shell. -195.974 -10.959 Td In Proceedings of the European Conference on Computer Vision (ECCV . Does not come with a power cord (this unit requires 2 cords!). /R21 80 0 R /R84 126 0 R << [ (mainly) -323.012 (come) -322.017 (from) -322.995 (its) -322.005 (tw) 10.0081 (o) -323 (k) 10.0032 (e) 15.0122 (y) -321.981 (f) 9.99343 (actors\054) -340.992 (i\056e\056\054) ] TJ /Resources << Apart from the, Training settings. 14.0367 2.10195 Td q [ (a) 10.0032 (g) 10.0032 (es) -356.997 (ar) 36.9852 (e) -357.984 (corrupted) -357.008 (by) -356.998 (multiple) -357.982 (types) -356.989 (of) -358.006 (de) 39.9933 (gr) 14.9901 (adation\056) -632.01 (Ex\055) ] TJ n /R255 312 0 R /R86 122 0 R 3) for LR features generated by projection groups to generate the output of FB: where CFF represents the function of Conv(1,m). /Resources << /ca 1 English. We choose two superior basic blocks (i.e. /R13 7.9701 Tf -83.9281 -24.7359 Td However, the feedforward manner makes it impossible for previous layers to access useful information from the following layers, even though skip connections are employed. The proposed SRFBN comes with a strong early reconstruction ability and can create the final high-resolution image step by step. S /R13 7.9701 Tf 115.593 0 Td We use bidirectional architecture, so our LBFN consists of two feedback procedures. Besides, for the img_092 from Urban100, However, they are usually treated as independent researches. /Resources << /R17 9.9626 Tf M.F. Stollenga, J.Masci, F.Gomez, and J.Schmidhuber. /R270 324 0 R The gate mechanisms in ConvLSTM influence the distribution and intensity of original images and thus are difficult to meet high fidelity needs in image SR tasks. 53.1684 0 Td Extensive experimental results demonstrate the superiority of the proposed SRFBN in comparison with the state-of-the-art methods. /R185 229 0 R In this paper, a gated multi-attention feedback network (GAMA) is proposed for medical image SR. [ (in) -417.016 (an) -416.008 (identical) -417.003 (LR) -415.992 (image\056) -810.003 (T) 79.9903 (o) -416.984 (address) -417.019 (this) -415.984 (problem\054) -458.992 (nu\055) ] TJ (skip) Tj BT [ (Inp) 3.01692 (ut) ] TJ Spatial-Spectral Feedback Network for Super-Resolution of Hyperspectral on FFHQ 512 x 512 - 4x upscaling. Learning a deep convolutional network for image super-resolution. -78.0617 -11.9551 Td /R27 4.74493 Tf /R17 76 0 R /R15 7.9701 Tf Arbona for polymer simulations and J. Parmar for suggestions that led to the name ANNA-PALM. /R244 289 0 R q 35:46. f* T* 2018. /R9 62 0 R 87.273 24.305 l 0.44706 0.57647 0.77255 rg (F) Tj /Contents 341 0 R However, all currently available methods focus on reconstructing texture details, resulting in blurred edges and incomplete structures in the reconstructed images. The Super-Resolution Feedback Network (SRFBN, 2019) [69] is also using feedback [114]. 3889.35 5237.93 l 4.73203 0 Td . /R78 107 0 R T* /R174 230 0 R /R8 gs In this paper, we propose a novel network for image SR called super-resolution feedback network (SRFBN) to faithfully reconstruct a SR image by enhancing low-level representations with high-level ones. 82.031 6.77 79.75 5.789 77.262 5.789 c projection units[11] and RDB[47]), which were designed for image SR task recently, and ConvLSTM from [40] for comparison. ET -50.1598 -11.9547 Td /Length 2400 SRFBN-L and SRFBN-LFF both have four iterations with four HR outputs. 5003.41 5378.29 m Z.Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. 1 0 0 1 297 35 Tm Q /ProcSet [ /ImageC /Text /PDF /ImageI /ImageB ] /R223 295 0 R /R252 323 0 R f [ (o) -0.49828 (u) -0.29815 (t) -0.80051 ] TJ 17.4391 TL 0 G /Type /Page >> SRFBN-L outperforms SRFBN-L-FF at every iteration, from which we conclude that the feedback network is capable of producing high quality early predictions in contrast to feedforward network. In this paper, we propose an image super-resolution feedback network (SRFBN) to refine low-level representations with high-level information. The LR feature extraction block consists of Conv(3,4m) and Conv(3,m). /R182 222 0 R In Tab. /R41 22 0 R T* >> Details about settings of target HR images for complex degradation models will be revealed in Sec. In addition, we introduce a curriculum learning strategy to make the network well suitable for more complicated tasks, where the low-resolution images are corrupted by multiple types of degradation. T* /R234 284 0 R << To reduce network parameters, the recurrent structure is often employed. 50.1598 0 Td 11.9551 TL W 11.9547 -12.7801 Td [ (Figure) -282.004 (1\056) -282.979 (The) -281.98 (illustrations) -283.015 (of) -282.015 (the) -283.012 (feedback) -282.004 (mechanism) -282.985 (in) -281.988 (the) -282.01 (pro\055) ] TJ endobj /R84 126 0 R [47] combined local/global residual and dense skip connections in their RDN. /XObject << >> /R34 27 0 R 11-22, we provide more visual results of different degradation models to prove the superiority of the proposed network. /R23 7.9701 Tf Ftin are then used as the input to the FB. f* /R256 319 0 R Deep learning-based networks have achieved great success in the field of image super-resolution. q 11.9551 TL Please see all pics. 1 0 0 1 153.269 675.067 Tm You are looking at a previously owned Marconi ASX-200BX ICP ATM Switch 8PT UTP5. proposed a texture transformer network for image super-resolution, where . /R25 4.74493 Tf 3959.21 5261.21 l 1 0 0 1 421.76 316.462 Tm [ (lum) -437.99 (learning) -436.99 (str) 14.9975 (ate) 40 (gy) -438.004 (to) -437.993 (mak) 10.002 (e) -438.01 (the) -437.018 (network) -438.018 (well) -437.983 (suitable) ] TJ /R184 226 0 R This further indicates that Ftout containing high-level information at the t-th iteration in the feedback network will urge previous layers at subsequent iterations to generate better representations. 4900.01 5438.11 l /R104 137 0 R /Resources << /Rotate 0 /R9 11.9552 Tf /R162 168 0 R /R17 76 0 R 0 Tc Code is avaliable at https://github.com/Paper99/SRFBN_CVPR19. 3282.69 5201.9 142.156 120.301 re /Group 42 0 R BT endobj ET /Parent 1 0 R /R11 11.9552 Tf To address this problem, numerous image SR methods have been proposed, including interpolation-based methods. /R175 233 0 R Q The information in our FB efficiently flows across hierarchical layers through dense skip connections. [ (The) -253.012 (second) -253.985 (f) 9.99588 (actor) -253.017 (can) -254 (ef) 25.0081 <026369656e746c79> -253.017 (alle) 24.9811 (viate) -253.987 (the) -253.007 (gradient) -254.016 (v) 24.9811 (an\055) ] TJ degradations. Specifically, we use hidden states in a recurrent neural network (RNN) with constraints to achieve such feedback manner. /Font << Cortical feedback improves discrimination between figure and /R11 65 0 R (\050b\051) Tj Q This work proposes a deep but compact convolutional network to directly reconstruct the high resolution image from the original low resolution image and demonstrates that the proposed method is superior to the state-of-the-art methods, especially in terms of time performance.
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