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              基于組信息蒸餾殘差網絡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-信息蒸餾殘差網絡的輕量級圖像超分辨率重建. 自動化學報, 2024, 50(10): 1?16 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絡的輕量級圖像超分辨率重建. 自動化學報, 2024, 50(10): 1?16 doi: 10.16383/j.aas.c211089
              Wang Yun-Tao, Zhao Lin, Liu Li-Man, Tao Wen-Bing. G-IDRN: An group-information distillation residual network for lightweight image super-resolution. Acta Automatica Sinica, 2024, 50(10): 1?16 doi: 10.16383/j.aas.c211089
              Citation: Wang Yun-Tao, Zhao Lin, Liu Li-Man, Tao Wen-Bing. G-IDRN: An group-information distillation residual network for lightweight image super-resolution. Acta Automatica Sinica, 2024, 50(10): 1?16 doi: 10.16383/j.aas.c211089

              基于組信息蒸餾殘差網絡的輕量級圖像超分辨率重建

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

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

                趙藺:華中科技大學人工智能與自動化學院博士研究生. 主要研究方向為圖像識別, 圖像超分辨率和點云實例語義分割. E-mail: linzhao@hust.edu.cn

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

                陶文兵:華中科技大學人工智能與自動化學院教授. 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: An Group-information Distillation Residual Network for Lightweight Image Super-resolution

              Funds: Supported by National Natural Science Foundation of China (61976227, 62176096) and National 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 University. 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 Technology. 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, 基于深度學習的超分辨算法已經取得了很好性能, 但這些方法通常具有較大的內存消耗和較高的計算復雜度, 很難應用到低算力或便攜式設備上. 為了解決這個問題, 設計一種輕量級的組?信息蒸餾殘差網絡(Group-information distillation residual network, G-IDRN)用于快速且精確的單圖像超分辨率. 具體來說, 提出一個更加有效的組?信息蒸餾模塊(Group-information distillation block, G-IDB)作為網絡特征提取基本塊. 同時, 引入密集快捷連接對多個基本塊進行組合, 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, 還提出一個輕量的非對稱殘差Non-local模塊, 對長距離依賴關系進行建模, 進一步提升超分的性能. 最后, 設計一個高頻損失函數, 去解決像素損失帶來圖像細節平滑的問題. 大量的實驗表明, 該算法相較于其他先進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, 其在公開測試數據集B100上, 4倍超分速率達到56 FPS, 比殘差注意力網絡快15倍.
              • 圖  1  Urban100數據集中, 圖像放大2倍的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 on Urban100 with 2 times factor

                圖  2  Urban100數據集中, img024放大4倍,不同SR方法在超分的重建結果

                Fig.  2  The reconstruction results of various SR methods for 4 times img024 on Urban100

                圖  3  組?信息蒸餾殘差網絡整體架構

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

                圖  4  G-IDB對RFDB的改進圖

                Fig.  4  G-IDB improvements to RFDB

                圖  5  非對稱殘差Non-Local模塊

                Fig.  5  The asymmetric Non-local residual block

                圖  6  Set14數據集中, barbara.png放大3倍的高頻提取圖像

                Fig.  6  High-frequency extraction images for 3 times barbara.png on Set14

                圖  7  HR圖像和對應使用低頻濾波器提取的低頻信息圖

                Fig.  7  HR images and their low-frequency information images extracted by 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 with differences 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  在真實圖像上的可視化對比結果

                Fig.  11  Visual comparison on a real-world image

                圖  12  Urban100上4倍因子時SSIM和參數量的比較結果

                Fig.  12  Comparison results of SSIM and the number of parameters for 4 times factors on Urban100

                表  1  消融實驗結果

                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中不同采樣特征點數的實驗結果

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

                特征點數Set5
                PSNR (dB)
                Manga109
                PSNR (dB)
                $128\times 128$像素
                內存 (MB)
                $180\times 180$像素
                內存 (MB)
                無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  不同算法在5個基準數據集上2、3和4倍因子的參數量、PSNR和SSIM定量比較

                Table  4  Parameters, PSNR and SSIM quantitative comparisons of various algorithms for 2, 3, and 4 times factors on the five benchmark data-sets

                方法放大
                尺度
                參數量
                (K)
                Set5
                PSNR (dB) / SSIM
                Set14
                PSNR (dB) / SSIM
                B100
                PSNR (dB) / SSIM
                Uban100
                PSNR (dB) / SSIM
                Manga109
                PSNR (dB) / SSIM
                Bicubic$2倍$?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 69438.03 / 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
                Bicubic$3倍$?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
                Bicubic$4倍$?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上4倍因子時FLOPs、PSNR和SSIM的比較結果

                Table  5  Comparison results of FLOPs, PSNR and SSIM for 4 times factors on Set14

                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上4倍因子時平均運行時間的比較結果

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

                方法PSNR (dB) / SSIM參數量 (K)訓練時間 (s)推理時間 (s)
                EDSR27.71 / 0.7420430900.2178
                RCAN27.77 / 0.7436155920.2596
                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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                        • 收稿日期:  2021-11-17
                        • 錄用日期:  2022-06-17
                        • 網絡出版日期:  2022-07-30

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