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              基于組?信息蒸餾殘差網(wǎng)絡(luò )的輕量級圖像超分辨率重建

              王云濤 趙藺 劉李漫 陶文兵

              王云濤, 趙藺, 劉李漫, 陶文兵. 基于組?信息蒸餾殘差網(wǎng)絡(luò )的輕量級圖像超分辨率重建. 自動(dòng)化學(xué)報, 2024, 50(10): 2063?2078 doi: 10.16383/j.aas.c211089
              引用本文: 王云濤, 趙藺, 劉李漫, 陶文兵. 基于組?信息蒸餾殘差網(wǎng)絡(luò )的輕量級圖像超分辨率重建. 自動(dòng)化學(xué)報, 2024, 50(10): 2063?2078 doi: 10.16383/j.aas.c211089
              Wang Yun-Tao, Zhao Lin, Liu Li-Man, Tao Wen-Bing. G-IDRN: A group-information distillation residual network for lightweight image super-resolution. Acta Automatica Sinica, 2024, 50(10): 2063?2078 doi: 10.16383/j.aas.c211089
              Citation: Wang Yun-Tao, Zhao Lin, Liu Li-Man, Tao Wen-Bing. G-IDRN: A group-information distillation residual network for lightweight image super-resolution. Acta Automatica Sinica, 2024, 50(10): 2063?2078 doi: 10.16383/j.aas.c211089

              基于組?信息蒸餾殘差網(wǎng)絡(luò )的輕量級圖像超分辨率重建

              doi: 10.16383/j.aas.c211089
              基金項目: 國家自然科學(xué)基金(61976227, 62176096), 湖北省自然科學(xué)基金(2019CFB622)資助
              詳細信息
                作者簡(jiǎn)介:

                王云濤:中南民族大學(xué)生物醫學(xué)工程學(xué)院碩士研究生. 主要研究方向為圖像處理, 深度學(xué)習和圖像超分辨率. E-mail: ytao-wang@scuec.edu.cn

                趙藺:華中科技大學(xué)人工智能與自動(dòng)化學(xué)院博士研究生. 主要研究方向為圖像識別, 圖像超分辨率和點(diǎn)云實(shí)例語(yǔ)義分割. E-mail: linzhao@hust.edu.cn

                劉李漫:中南民族大學(xué)生物醫學(xué)工程學(xué)院副教授. 主要研究方向為圖像處理, 深度學(xué)習和計算機視覺(jué). 本文通信作者. E-mail: limanliu@mail.scuec.edu.cn

                陶文兵:華中科技大學(xué)人工智能與自動(dòng)化學(xué)院教授. 主要研究方向為圖像分割, 目標識別和3D重建. E-mail: wenbingtao@hust.edu.cn

              G-IDRN: A Group-information Distillation Residual Network for Lightweight Image Super-resolution

              Funds: Supported by National Natural Science Foundation of China (61976227, 62176096) and Natural Science Foundation of Hubei Province (2019CFB622)
              More Information
                Author Bio:

                WANG Yun-Tao Master student at the School of Biomedical Engineering, South-central Minzu Univ-ersity. His research interest covers image processing, deep learning, and image super-resolution

                ZHAO Lin Ph.D. candidate at the School of Artificial Intelligence and Automation, Huazhong University of Science and Tech-nology. His research interest covers image recognition, image super-resolution, and point cloud instance semantic segmentation

                LIU Li-Man Associate professor at the School of Biomedical Engineering, South-central Minzu University. Her research interest covers image processing, deep learning, and computer vision. Corresponding author of this paper

                TAO Wen-Bing Professor at the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology. His research interest covers image segmentation, target recognition, and 3D reconstruction

              • 摘要: 目前, 基于深度學(xué)習的超分辨算法已經(jīng)取得了很好性能, 但這些方法通常具有較大內存消耗和較高計算復雜度, 很難應用到低算力或便攜式設備上. 為了解決這個(gè)問(wèn)題, 設計一種輕量級的組?信息蒸餾殘差網(wǎng)絡(luò )(Group-information distillation residual network, G-IDRN)用于快速且精確的單圖像超分辨率任務(wù). 具體地, 提出一個(gè)更加有效的組?信息蒸餾模塊(Group-information distillation block, G-IDB)作為網(wǎng)絡(luò )特征提取基本塊. 同時(shí), 引入密集快捷連接, 對多個(gè)基本塊進(jìn)行組合, 構建組?信息蒸餾殘差組(Group-information distillation residual group, G-IDRG), 捕獲多層級信息和有效重利用特征. 另外, 還提出一個(gè)輕量的非對稱(chēng)殘差Non-local模塊, 對長(cháng)距離依賴(lài)關(guān)系進(jìn)行建模, 進(jìn)一步提升超分性能. 最后, 設計一個(gè)高頻損失函數, 去解決像素損失帶來(lái)圖像細節平滑的問(wèn)題. 大量實(shí)驗結果表明, 該算法相較于其他先進(jìn)方法, 可以在圖像超分辨率性能和模型復雜度之間取得更好平衡, 其在公開(kāi)測試數據集B100上, 4倍超分速率達到56 FPS, 比殘差注意力網(wǎng)絡(luò )快15倍.
              • 圖  1  Urban100中圖像放大2倍時(shí), 參數量和峰值信噪比的對比結果

                Fig.  1  Comparison results of the number of parameters and the peak single-to-noise ration for 2 times images on Urban100

                圖  2  Urban100中Img024放大4倍時(shí), 不同SR方法的重建結果

                Fig.  2  The reconstruction results of various SR methods for 4 times Img024 on Urban100

                圖  3  組?信息蒸餾殘差網(wǎng)絡(luò )整體架構

                Fig.  3  The architecture of the group-information distillation residual network

                圖  4  G-IDB對RFDB的改進(jìn)圖

                Fig.  4  G-IDB improvements to RFDB

                圖  5  非對稱(chēng)殘差Non-local模塊

                Fig.  5  The asymmetric Non-local residual block

                圖  6  Set14中barbara.png放大3倍的高頻提取圖像((a)裁剪的 HR 圖像; (b) HR 圖像的高頻提取圖; (c)裁剪的SR圖像; (d) SR圖像的高頻提取圖)

                Fig.  6  High-frequency extraction images for 3 times barbara.png on Set14 ((a) Cropped HR image; (b) High-frequency extractionimage of HR image; (c) Cropped SR imag; (d) High-frequency extraction image of SR image)

                圖  7  HR圖像和對應使用高斯低通濾波器提取的低頻信息圖

                Fig.  7  HR images and their low-frequency information images extracted by the Gaussian low-pass filter

                圖  8  使用不同損失權重系數的PSNR分數差值對比結果

                Fig.  8  Comparison results of PSNR score differences with different loss weights

                圖  9  PSNR和SSIM的差值圖

                Fig.  9  Differential results of PSNR and SIMM scores

                圖  10  各方法在Urban100上4倍SR的定性比較

                Fig.  10  Qualitative comparisons of each method for 4 times SRs on Urban100

                圖  11  在真實(shí)圖像上的可視化對比結果

                Fig.  11  Visual comparison on a real-world image

                圖  12  Urban100中圖像放大4倍時(shí), 參數量和結構相似度的對比結果

                Fig.  12  Comparison results of the number of parameters and the structural similarity for 4 times images on Urban100

                表  1  消融實(shí)驗結果

                Table  1  Ablation experiment results

                基本塊雙路重建策略DS連接ANRBPSNR (dB)參數量 (K)增幅PSNR (dB) | 參數量 (K)
                RFDB???37.893534.00 | 0
                $ \checkmark$??37.931514.2$\uparrow$ 0.038 | $\downarrow$ 19.8
                ?$ \checkmark$?37.891520.2$ \downarrow$ 0.002 | $ \downarrow$ 13.8
                ??$ \checkmark$37.916534.3$ \uparrow$ 0.023 | $ \uparrow$ 0.3
                $ \checkmark$?$ \checkmark$37.934514.4$ \uparrow$ 0.041 | $ \downarrow$ 19.6
                $ \checkmark$$ \checkmark$$ \checkmark$37.940500.5$ \uparrow$ 0.047 | $ \downarrow$ 33.5
                G-IDB???37.955449.4$ \uparrow$ 0.062 | $ \downarrow$ 84.6
                $ \checkmark$$ \checkmark$$ \checkmark$37.965383.2$ \uparrow$ 0.072 | $ \downarrow$ 150.8
                下載: 導出CSV

                表  2  ANRB中, 不同采樣特征點(diǎn)數的實(shí)驗結果

                Table  2  The experimental results for different sampled feature points in ANRB

                特征點(diǎn)數Set5
                PSNR (dB)
                Manga109
                PSNR (dB)
                $128\times 128$像素
                內存消耗 (MB)
                $180\times 180$像素
                內存消耗 (MB)
                無(wú)ANRB37.88838.396216419
                $S=50$37.89338.439224436
                $S=110$37.89538.443232452
                $S=222$37.86138.325246480
                $S=\infty$37.883內存溢出22668431
                下載: 導出CSV

                表  3  使用不同損失權重系數的PSNR對比結果 (dB)

                Table  3  Comparison results of PSNR with different loss weights (dB)

                權重系數Set5Set14B100Urban100Manga109
                $\alpha =1.0$, $\beta =0$37.90733.42332.06331.83038.483
                $\alpha =0.8$, $\beta =0.2$37.90033.40632.07131.85038.476
                $\alpha =0.6$, $\beta =0.4$37.93033.42132.07531.84338.483
                $\alpha =0.4$, $\beta =0.6$37.97533.44432.08431.87838.576
                $\alpha =0.2$, $\beta =0.8$37.90133.46732.08431.86038.462
                下載: 導出CSV

                表  4  在5個(gè)基準數據集上, 圖像放大2倍、3倍和4倍時(shí), 各算法的參數量、PSNR和SSIM定量分析結果

                Table  4  Parameters, PSNR and SSIM quantitative comparisons of various algorithms for 2, 3, and 4 times images on the five benchmark datasets

                方法
                放大
                倍數
                參數量
                (K)
                Set5
                PSNR (dB) / SSIM
                Set14
                PSNR (dB) / SSIM
                B100
                PSNR (dB) / SSIM
                Urban100
                PSNR (dB) / SSIM
                Manga109
                PSNR (dB) / SSIM
                Bicubic2倍? 33.66 / 0.929930.24 / 0.868829.56 / 0.843126.88 / 0.840330.80 / 0.9339
                SRCNN 836.66 / 0.954232.45 / 0.906731.36 / 0.887929.50 / 0.894635.60 / 0.9663
                DRCN 177437.63 / 0.958833.04 / 0.911831.85 / 0.894230.75 / 0.913337.55 / 0.9732
                LapSRN 25137.52 / 0.959132.99 / 0.912431.80 / 0.895230.41 / 0.910337.27 / 0.9740
                DRRN 29837.74 / 0.959133.23 / 0.913632.05 / 0.897331.23 / 0.918837.88 / 0.9749
                MemNet 67837.78 / 0.959733.28 / 0.914232.08 / 0.897831.31 / 0.919537.72 / 0.9740
                IDN 55337.83 / 0.960033.30 / 0.914832.08 / 0.898531.27 / 0.919638.01 / 0.9749
                SRMDNF 151137.79 / 0.960133.32 / 0.915932.05 / 0.898531.33 / 0.920438.07 / 0.9761
                CARN 159237.76 / 0.959033.52 / 0.916632.09 / 0.897831.92 / 0.925638.36 / 0.9765
                SMSR 98538.00 / 0.960133.64 / 0.917932.17 / 0.899332.19 / 0.928438.76 / 0.9771
                IMDN 69438.00 / 0.960533.63 / 0.917732.19 / 0.899732.17 / 0.928238.88 / 0.9774
                IMDN-JDSR 69438.00 / 0.960533.57 / 0.917632.16 / 0.899532.09 / 0.9271? / ?
                PAN 26138.00 / 0.960533.59 / 0.918132.18 / 0.899732.01 / 0.927338.70 / 0.9773
                RFDN-L 62638.03 / 0.960633.65 / 0.918332.18 / 0.899732.16 / 0.928238.88 / 0.9772
                LatticeNet 75938.03 / 0.960733.70 / 0.918732.20 / 0.899932.25 / 0.9288? / ?
                G-IDRN 55438.09 / 0.960833.80 / 0.920332.42 / 0.900332.42 / 0.931138.96 / 0.9773
                Bicubic3倍? 30.39 / 0.868227.55 / 0.774227.21 / 0.738524.46 / 0.734926.95 / 0.8556
                SRCNN 832.75 / 0.909029.30 / 0.821528.41 / 0.786326.24 / 0.798930.48 / 0.9117
                DRCN 177433.82 / 0.922629.76 / 0.831128.80 / 0.796327.15 / 0.827632.24 / 0.9343
                LapSRN 50233.81 / 0.922029.79 / 0.832528.82 / 0.798027.07 / 0.827532.21 / 0.9350
                DRRN 29834.03 / 0.924429.96 / 0.834928.95 / 0.800427.53 / 0.837832.71 / 0.9379
                MemNet 67834.09 / 0.924830.00 / 0.835028.96 / 0.800127.56 / 0.837632.51 / 0.9369
                IDN 55334.11 / 0.925329.99 / 0.835428.95 / 0.801327.42 / 0.835932.71 / 0.9381
                SRMDNF 152834.12 / 0.925430.04 / 0.838228.97 / 0.802527.57 / 0.839833.00 / 0.9403
                CARN 159234.29 / 0.925530.29 / 0.840729.06 / 0.803428.06 / 0.849333.50 / 0.9440
                SMSR 99334.40 / 0.927030.33 / 0.841229.10 / 0.805028.25 / 0.853633.68 / 0.9445
                IMDN 70334.36 / 0.927030.32 / 0.841729.09 / 0.804728.16 / 0.851933.61 / 0.9445
                IMDN-JDSR 70334.36 / 0.926930.32 / 0.841329.08 / 0.804528.12 / 0.8498? / ?
                PAN 26134.40 / 0.927130.36 / 0.842329.11 / 0.805028.11 / 0.851133.61 / 0.9448
                RFDN-L 63334.39 / 0.927130.35 / 0.841929.11 / 0.805428.24 / 0.853433.74 / 0.9453
                LatticeNet 76534.40 / 0.927230.32 / 0.841629.10 / 0.804928.19 / 0.8513? / ?
                G-IDRN 56534.43 / 0.927730.41 / 0.843129.14 / 0.806128.32 / 0.855233.79 / 0.9456
                Bicubic4倍?28.42 / 0.810426.00 / 0.702725.96 / 0.667523.14 / 0.657724.89 / 0.7866
                SRCNN 830.48 / 0.862627.50 / 0.751326.90 / 0.710124.52 / 0.722127.58 / 0.8555
                DRCN 177431.53 / 0.885428.02 / 0.767027.23 / 0.723325.14 / 0.751028.93 / 0.8854
                LapSRN 50231.54 / 0.885228.09 / 0.770027.32 / 0.727525.21 / 0.756229.09 / 0.8900
                DRRN 29831.68 / 0.888828.21 / 0.772027.38 / 0.728425.44 / 0.763829.45 / 0.8946
                MemNet 67831.74 / 0.889328.26 / 0.772327.40 / 0.728125.50 / 0.763029.42 / 0.8942
                IDN 55331.82 / 0.890328.25 / 0.773027.41 / 0.729725.41 / 0.763229.41 / 0.8942
                SRMDNF 155231.96 / 0.892528.35 / 0.778727.49 / 0.733725.68 / 0.773130.09 / 0.9024
                CARN 159232.13 / 0.893728.60 / 0.780627.58 / 0.734926.07 / 0.783730.47 / 0.9084
                SMSR 100632.13 / 0.893728.60 / 0.780627.58 / 0.734926.11 / 0.786830.54 / 0.9084
                IMDN 71532.21 / 0.894828.58 / 0.781127.56 / 0.735426.04 / 0.783830.45 / 0.9075
                IMDN-JDSR 71532.17 / 0.894228.62 / 0.781427.55 / 0.735026.06 / 0.7820? / ?
                PAN 27232.13 / 0.894828.61 / 0.782227.59 / 0.736326.11 / 0.785430.51 / 0.9095
                RFDN-L 64332.23 / 0.895328.59 / 0.781427.57 / 0.736326.14 / 0.787130.61 / 0.9095
                LatticeNet 77732.18 / 0.894328.61 / 0.781227.57 / 0.735526.14 / 0.7844? / ?
                G-IDRN 58032.24 / 0.895828.64 / 0.782427.61 / 0.737826.24 / 0.790330.63 / 0.9096
                下載: 導出CSV

                表  5  Set14中圖像放大4倍時(shí), SSIM、PSNR和FLOPs的比較結果

                Table  5  Comparison results of SSIM、PSNR andFLOPs for 4 times images on Set14

                評價(jià)指標CARNIMDNRFDN-LG-IDRN
                SSIM0.78060.78100.78140.7826
                PSNR (dB) 28.6028.5828.5928.64
                FLOPs (GB)103.5846.6041.5436.19
                下載: 導出CSV

                表  6  B100中圖像放大4倍時(shí), 平均運行時(shí)間的比較結果

                Table  6  Comparison results of average running time for4 times images on B100

                方法PSNR (dB) / SSIM參數量 (K)訓練時(shí)間 (s)推理時(shí)間 (s)
                EDSR27.71 / 0.742043090 0.2178
                RCAN27.77 / 0.743615592— 0.2596
                IMDN27.56 / 0.73547155.40.0217
                RFDN-L27.57 / 0.73636336.10.0250
                G-IDRN27.61 / 0.737858012.70.0177
                IDRN27.64 / 0.738920478.50.0692
                下載: 導出CSV
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                      1. [1] Isaac J S, Kulkarni R. Super resolution techniques for medical image processing. In: Proceedings of the International Conference on Technologies for Sustainable Development. Mumbai, India: IEEE, 2015. 1?6
                        [2] Rasti P, Uiboupin T, Escalera S, Anbarjafari G. Convolutional neural network super resolution for face recognition in surveillance monitoring. In: Proceedings of the International Conference on Articulated Motion and Deformable Objects. Cham, Netherlands: Springer, 2016. 175?184
                        [3] Sajjadi M S M, Scholkopf B, Hirsch M. Enhancenet: Single image super-resolution through automated texture synthesis. In: Proceedings of the IEEE International Conference on Computer Vision. Venice, Italy: IEEE, 2017. 4491?4500
                        [4] Tan Y, Cai J, Zhang S, Zhong W, Ye L. Image compression algorithms based on super-resolution reconstruction technology. In: Proceedings of the IEEE 4th International Conference on Image, Vision and Computing. Xiamen, China: IEEE, 2019. 162? 166
                        [5] Luo Y, Zhou L, Wang S, Wang Z. Video satellite imagery super resolution via convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 2017, 14(12): 2398?2402 doi: 10.1109/LGRS.2017.2766204
                        [6] 楊欣, 周大可, 費樹(shù)岷. 基于自適應雙邊全變差的圖像超分辨率重建. 計算機研究與發(fā)展, 2012, 49(12): Article No. 2696

                        Yang Xin, Zhou Da-Ke, Fei Shu-Min. A self-adapting bilateral total aariation technology for image super-resolution reconstruction. Journal of Computer Research and Development, 2012, 49(12): Article No. 2696
                        [7] Zhang L, Wu X. An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Transactions on Image Processing, 2006, 15(8): 2226?2238 doi: 10.1109/TIP.2006.877407
                        [8] 潘宗序, 禹晶, 胡少興, 孫衛東. 基于多尺度結構自相似性的單幅圖像超分辨率算法. 自動(dòng)化學(xué)報, 2014, 40(4): 594?603

                        Pan Zong-Xu, Yu Jing, Hu Shao-Xing, Sun Wei-Dong. Single image super resolution based on multi-scale structural self-similarity. Acta Automatica Sinica, 2014, 40(4): 594?603
                        [9] 張毅鋒, 劉袁, 蔣程, 程旭. 用于超分辨率重建的深度網(wǎng)絡(luò )遞進(jìn)學(xué)習方法. 自動(dòng)化學(xué)報, 2020, 40(2): 274?282

                        Zhang Yi-Feng, Liu Yuan, Jiang Cheng, Cheng Xu. A curriculum learning approach for single image super-resolution. Acta Automatica Sinica, 2020, 40(2): 274?282
                        [10] Dai T, Cai J, Zhang Y, Xia S T, Zhang L. Second-order attention network for single image super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE, 2019. 11065?11074
                        [11] Hui Z, Gao X, Yang Y, Wang X. Lightweight image super-resolution with information multi-distillation network. In: Proceedings of the 27th ACM International Conference on Multimedia. New York, USA: Association for Computing Machinery, 2019. 2024?2032
                        [12] Liu J, Tang J, Wu G. Residual feature distillation network for lightweight image super-resolution. In: Proceedings of the 20th European Conference on Computer Vision. Cham, Netherlands: Springer, 2020. 41?55
                        [13] 孫超文, 陳曉. 基于多尺度特征融合反投影網(wǎng)絡(luò )的圖像超分辨率重建. 自動(dòng)化學(xué)報, 2021, 47(7): 1689?1700

                        Sun Chao-Wen, Chen Xiao. Multi-scale feature fusion back-projection network for image super-resolution. Acta Automatica Sinica, 2021, 47(7): 1689?1700
                        [14] 孫玉寶, 費選, 韋志輝, 肖亮. 基于前向后向算子分裂的稀疏性正則化圖像超分辨率算法. 自動(dòng)化學(xué)報, 2010, 36(9): 1232?1238 doi: 10.3724/SP.J.1004.2010.01232

                        Sun Yu-Bao, Fei Xuan, Wei Zhi-Hui, Xiao Liang. Sparsity regularized image super-resolution model via forward-backward operator splitting method. Acta Automatica Sinica, 2010, 36(9): 1232?1238 doi: 10.3724/SP.J.1004.2010.01232
                        [15] Dong C, Loy C C, He K, Tang X. Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern An-alysis and Machine Intelligence, 2015, 38(2): 295?307
                        [16] Dong C, Loy C C, Tang X. Accelerating the super-resolution convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Veg-as, USA: IEEE, 2016. 391?407
                        [17] Kim J, Lee J K, Lee K M. Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016. 1646?1654
                        [18] Kim J, Lee J K, Lee K M. Deeply-recursive convolutional network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016. 1637?1645
                        [19] Lim B, Son S, Kim H, Nah S, Mu Lee K. Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. Honolulu, USA: IEEE, 2017. 136?144
                        [20] Zhang Y, Li K, Li K, Wang L, Zhong B, Fu Y. Image super-resolution using very deep residual channel attention networks. In: Proceedings of the 18th European Conference on Computer Vision. Mohini, Germany: Springer, 2018. 286?301
                        [21] Ahn N, Kang B, Sohn K A. Fast, accurate, and lightweight super-resolution with cascading residual network. In: Proceedings of the 18th European Conference on Computer Vision. Mohini, Germany: Springer, 2018. 252?268
                        [22] Hui Z, Wang X, Gao X. Fast and accurate single image super-resolution via information distillation network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE, 2018. 723?731
                        [23] Zhang C, Benz P, Argaw D M, Lee S, Kim J, Rameau F, et al. Resnet or densenet? Introducing dense shortcuts to resnet. In: Proceedings of the IEEE/CVF Winter Conference on Applicati-ons of Computer Vision. Waikoloa, USA: IEEE, 2021. 3550?3559
                        [24] Zhu Z, Xu M, Bai S, Huang T, Bai X. Asymmetric non-local neural networks for semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE, 2019. 593?602
                        [25] Huang J B, Singh A, Ahuja N. Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Bos-ton, USA: IEEE, 2015. 5197?5206
                        [26] 安耀祖, 陸耀, 趙紅. 一種自適應正則化的圖像超分辨率算法. 自動(dòng)化學(xué)報, 2012, 38(4): 601?608 doi: 10.3724/SP.J.1004.2012.00601

                        An Yao-Zu, Lu Yao, Zhao Hong. An adaptive-regularized image super-resolution. Acta Automatica Sinica, 2012, 38(4): 601?608 doi: 10.3724/SP.J.1004.2012.00601
                        [27] Tai Y, Yang J, Liu X, Xu C. MemNet: A persistent memory network for image restoration. In: Proceedings of the IEEE International Conference on Computer Vision. Venice, Italy: IEEE, 2017. 4539?4547
                        [28] Li Z, Yang J, Liu Z, Jeon G, Wu W. Feedback network for image super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE, 2019. 3867?3876
                        [29] Qiu Y, Wang R, Tao D, Cheng J. Embedded block residual network: A recursive restoration model for single-image super-resolution. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. Seoul, South Korea: IEEE, 2019. 4180?4189
                        [30] Chu X, Zhang B, Ma H, Xu R, Li Q. Fast, accurate and lightweight super-resolution with neural architecture search. In: Proceedings of the 25th International Conference on Pattern Recognition. Milan, Italy: IEEE, 2021. 59?64
                        [31] Chu X, Zhang B, Xu R. Multi-objective reinforced evolution in mobile neural architecture search. In: Proceedings of the 20th European Conference on Computer Vision. Glasgow, UK: Sprin-ger, 2020. 99?113
                        [32] Luo X, Xie Y, Zhang Y, Qu Y, Li C, Fu Y. LatticeNet: Towards lightweight image super-resolution with lattice block. In: Proceedings of the 20th European Conference on Computer Vision. Glasgow, UK: Springer, 2020. 23?28
                        [33] Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City, USA: IEEE, 2018. 7132?7141
                        [34] Wang X, Girshick R, Gupta A, He K. Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City, USA: IEEE, 2018. 7794?7803
                        [35] Liu D, Wen B, Fan Y, Loy C C, Huang T S. Non-local recurrent network for image restoration. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. Montréal, Canada: MIT Press, 2018. 1680–1689
                        [36] Mei Y, Fan Y, Zhou Y, Huang L, Huang T S, Shi H. Image super-resolution with cross-scale non-local attention and exhaustive self-exemplars mining. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Sea-ttle, USA: IEEE, 2020. 5690?5699
                        [37] Niu B, Wen W, Ren W, Zhang X, Yang L, Wang S, et al. Single image super-resolution via a holistic attention network. In: Proceedings of the 20th European Conference on Computer Vision. Glasgow, UK: Springer, 2020. 191?207
                        [38] Johnson J, Alahi A, Li F F. Perceptual losses for real-time style transfer and super-resolution. In: Proceedings of the 14th Eur-opean Conference on Computer Vision. Amsterdam, Netherlands: Springer, 2016. 694?711
                        [39] Ledig C, Theis L, Huszár F, Caballero J, Cunningham A, Acosta A, et al. Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017. 4681?4690
                        [40] Yuan Y, Liu S, Zhang J, Zhang Y, Dong C, Lin L. Unsupervised image super-resolution using cycle-in-cycle generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City, USA: IEEE, 2018. 701?710
                        [41] Yu J, Fan Y, Huang T. Wide activation for efficient image and video super-resolution. In: Proceedings of the 30th British Machine Vision Conference. Cardiff, UK: BMVA Press, 2020. 1?13
                        [42] Shi W, Caballero J, Huszár F, Totz J, Aitken A P, Bishop R, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016. 1874?1883
                        [43] Howard A G, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, et al. MobileNets: Efficient convolutional neural networks for mobile vision applications [Online], available: https://arxiv.org/abs/1704.04861, April 17, 2017
                        [44] Xie S, Girshick R, Dollár P, Tu Z, He K. Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017. 1492?1500
                        [45] Szegedy C, Ioffe S, Vanhoucke V, Alemi A A. Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence. San Francisco, USA: AAAI Press, 2017. 4278–4284
                        [46] Huang G, Liu Z, Van Der Maaten L, Weinberger K Q. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017. 4700?4708
                        [47] Zhao H, Shi J, Qi X, Wang X, Jia J. Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017. 2881? 2890
                        [48] Timofte R, Agustsson E, Van Gool L, Yang M H, Zhang L. Ntire 2017 challenge on single image super-resolution: Methods and results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. Honolulu, USA: IEEE, 2017. 114?125
                        [49] Bevilacqua M, Roumy A, Guillemot C, Morel M L A. Lowcomplexity single-image super-resolution based on nonnegative neighbor embedding. In: Proceedings of the British Machine Vision Conference. Surrey, UK: BMVA Press, 2012. 1?10
                        [50] Zeyde R, Elad M, Protter M. On single image scale-up using sparse-representations. In: Proceedings of the International Conference on Curves and Surfaces. Berlin, Germany: Springer, 2010. 711?730
                        [51] Arbelaez P, Maire M, Fowlkes C, Malik J. Contour detection and hierarchical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 33(5): 898?916
                        [52] Matsui Y, Ito K, Aramaki Y, Fujimoto A, Ogawa T, Yamasaki T, et al. Sketch-based manga retrieval using Manga109 dataset. Multimedia Tools and Applications, 2017, 76(20): 21811?21838 doi: 10.1007/s11042-016-4020-z
                        [53] Gao X, Lu W, Tao D, Li X. Image quality assessment based on multi-scale geometric analysis. IEEE Transactions on Image Processing, 2009, 18(7): 1409?1423 doi: 10.1109/TIP.2009.2018014
                        [54] Wang Z, Bovik A C, Sheikh H R, Simoncelli E P. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 2004, 13(4): 600?612 doi: 10.1109/TIP.2003.819861
                        [55] Chollet F. Xception: Deep learning with depth-wise separable convolutions. In: Proceedings of the IEEE International Conference on Computer Vision. Venice, Italy: IEEE, 2017. 1251?1258
                        [56] Lai W S, Huang J B, Ahuja N, Yang M H. Deep Laplacian pyramid networks for fast and accurate super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017. 624?632
                        [57] Tai Y, Yang J, Liu X. Image super-resolution via deep recursive residual network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017. 3147?3155
                        [58] Zhang K, Zuo W, Zhang L. Learning a single convolutional super-resolution network for multiple degradations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City, USA: IEEE, 2018. 3262?3271
                        [59] Wang L, Dong X, Wang Y, Ying X, Lin Z, An W, et al. Exploring sparsity in image super-resolution for efficient inference. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville, USA: IEEE, 2021. 4917?4926
                        [60] Luo X, Liang Q, Liu D, Qu Y. Boosting lightweight single image super-resolution via joint-distillation. In: Proceedings of the 29th ACM International Conference on Multimedia. Virtual Event: Association for Computing Machinery, 2021. 1535?1543
                        [61] Zhao H, Kong X, He J, Qiao Y, Dong C. Efficient image super-resolution using pixel attention. In: Proceedings of the European Conference on Computer Vision. Cham, Netherlands: Springer, 2020. 56?72
                        [62] Cai J, Zeng H, Yong H, Cao Z, Zhang L. Toward real-world single image super-resolution: A new benchmark and a new model. In: Proceedings of IEEE/CVF International Conference on Computer Vision. Seoul, South Korea: IEEE, 2019. 3086?3095
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                        出版歷程
                        • 收稿日期:  2021-11-17
                        • 錄用日期:  2022-06-17
                        • 網(wǎng)絡(luò )出版日期:  2022-07-30
                        • 刊出日期:  2024-10-21

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