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梯度引导的JPEG压缩图像超分辨率重建

曹坪 林树冉 张淳杰 郑晓龙 赵耀

曹坪, 林树冉, 张淳杰, 郑晓龙, 赵耀. 梯度引导的JPEG压缩图像超分辨率重建. 自动化学报, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c240517
引用本文: 曹坪, 林树冉, 张淳杰, 郑晓龙, 赵耀. 梯度引导的JPEG压缩图像超分辨率重建. 自动化学报, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c240517
Cao Ping, Lin Shu-Ran, Zhang Chun-Jie, Zheng Xiao-Long, Zhao Yao. Gradient-guided super-resolution reconstruction for JPEG-compressed images. Acta Automatica Sinica, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c240517
Citation: Cao Ping, Lin Shu-Ran, Zhang Chun-Jie, Zheng Xiao-Long, Zhao Yao. Gradient-guided super-resolution reconstruction for JPEG-compressed images. Acta Automatica Sinica, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c240517

梯度引导的JPEG压缩图像超分辨率重建

doi: 10.16383/j.aas.c240517 cstr: 32138.14.j.aas.c240517
基金项目: 国家自然科学基金(62476021, 72225011, 72434005, 62072026), 多模态人工智能系统全国重点实验室开放课题基金(MAIS2024106)
详细信息
    作者简介:

    曹坪:北京交通大学计算机科学与技术学院信息科学研究所博士研究生. 主要研究方向为目标检测, 语义分割和低质量视觉重建. E-mail: pingcao@bjtu.edu.cn

    林树冉:北京交通大学计算机科学与技术学院信息科学研究所硕士研究生. 主要研究方向为图像超分辨率重建. E-mail: 21120292@bjtu.edu.cn

    张淳杰:北京交通大学计算机科学与技术学院信息科学研究所教授. 主要研究方向为图像处理与理解, 计算机视觉和多媒体数据处理与分析. 本文通讯作者. E-mail: cjzhang@bjtu.edu.cn

    郑晓龙:中国科学院自动化研究所研究员. 主要研究方向为大数据与社会计算, 多模态数据感知与理解. E-mail: xiaolong.zheng@ia.ac.cn

    赵耀:北京交通大学计算机科学与技术学院信息科学研究所教授. 主要研究方向为图像/视频编码, 数字水印和取证, 视频分析和理解. E-mail: yzhao@bjtu.edu.cn

Gradient-guided Super-resolution Reconstruction for JPEG-compressed Images

Funds: Supported by National Natural Science Foundation of China (62476021, 72225011, 72434005, 62072026) and Open Projects Program of State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS2024106)
More Information
    Author Bio:

    CAO Ping Ph. D. candidate at the Institute of Information Science, School of Computer Science and Technology, Beijing Jiaotong University. Her research interest covers object detection, semantic segmentation and low-quality visual reconstruction

    LIN Shu-Ran Master student at the Institute of Information Science, School of Computer Science and Technology, Beijing Jiaotong University. His main research interest is image super-resolution reconstruction

    ZHANG Chun-Jie Professor at the Institute of Information Science, School of Computer Science and Technology, Beijing Jiaotong University. His research interest covers image processing and understanding, computer vision and multimedia data processing and analysis. Corresponding author of this paper

    ZHENG Xiao-Long Professor at the Institute of Automation, Chinese Academy of Sciences. His research interest covers big data and social computing, multimodal data perception and understanding

    ZHAO Yao Professor at the Institute of Information Science, School of Computer Science and Technology, Beijing Jiaotong University. His research interest covers image/video coding, digital watermarking and forensics, video analysis and understanding

  • 摘要: 在真实场景中, 图像往往同时遭受低分辨率、压缩失真及噪声等多种退化因素影响. 现有方法通常聚焦于单一退化类型, 难以应对复杂的复合退化情况. 为解决真实场景中普遍存在的低分辨率与JPEG压缩伪影复合退化问题, 提出一种梯度引导的联合JPEG压缩伪影去除和超分辨率重建网络. 该网络以超分辨率分支为主导, 融合JPEG压缩伪影去除分支与梯度引导分支的非对称特征, 实现了高质量图像重建. JPEG压缩伪影去除分支专注于压缩伪影抑制, 缓解了主导分支的重建负担. 梯度引导分支则精准估计图像梯度, 引导主导分支恢复更多细节与纹理. 实验结果表明, 该方法提升了低分辨率JPEG压缩图像的重建质量.
  • 图  1  JPEG压缩图像示例

    Fig.  1  Examples of JPEG-compressed images

    图  2  JCARSR的网络结构

    Fig.  2  Network architecture of JCARSR

    图  3  多尺度无非线性激活模块

    Fig.  3  Multi-scale nonlinear activation free block

    图  4  混合注意力模块

    Fig.  4  Mix attention module

    图  5  多尺度交叉卷积组

    Fig.  5  Muti-scale cross convolutional group

    图  6  不同模型的性能与运行时间对比

    Fig.  6  Comparison of performance and runtime across different models.

    图  7  JPEG压缩图像超分辨率视觉对比

    Fig.  7  Visual comparison of JPEG-compressed image super-resolution

    图  8  两阶段JPEG压缩图像超分辨率视觉对比

    Fig.  8  Visual comparison of two-stage JPEG-compressed image super-resolution

    图  9  在真实数据集上的超分辨率视觉对比

    Fig.  9  Visual comparison of super-resolution on real-world datasets

    图  10  梯度引导视觉分析

    Fig.  10  Visual analysis of gradient guidance.

    表  1  JCARSR与经典图像超分辨率方法的定量比较

    Table  1  Quantitative comparison of JCARSR with classical image super-resolution methods

    对比方法 尺度因子 模型参数(M) Set5 Set14 Urban100 B100 Manga109
    PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM
    EDSR[27] 2 40.73 28.97/0.8235 26.85/0.7136 25.07/0.7368 26.14/0.6623 28.04/0.8560
    RDN[28] 2 22.12 28.92/0.8227 26.82/0.7126 24.97/0.7335 26.11/0.6613 27.98/0.8548
    RCAN[29] 2 15.44 29.00/0.823 6 26.83/0.7125 25.08/0.7355 26.13/0.6610 28.02/0.8550
    MDCN[30] 2 13.12 28.91/0.8220 26.80/0.7120 24.92/0.7311 26.11/0.6607 27.91/0.8530
    SwinIR[46] 2 11.75 28.95/0.8231 26.87/0.714 1 25.11/0.737 7 26.13/0.6622 28.03/0.856 5
    DRCT [55] 2 13.34 28.86/0.8207 26.76/0.7103 24.87/0.7275 26.08/0.6597 27.78/0.8510
    HMA[56] 2 66.49 28.79/0.8183 26.71/0.7077 24.73/0.7214 26.04/0.6575 27.64/0.8471
    JCARSR 2 10.10 29.13/0.8269 26.96/0.7158 25.28/0.7423 26.19/0.6637 28.37/0.8609
    EDSR[27] 4 43.09 25.03/0.7118 23.91/0.6005 21.87/0.5891 23.86/0.5588 23.26/0.7270
    RDN[28] 4 22.27 25.04/0.7128 23.89/0.6012 21.84/0.5881 23.84/0.5593 23.25/0.7276
    RCAN[29] 4 15.59 25.07/0.715 5 23.90/0.6010 21.93/0.5910 23.85/0.5592 23.26/0.7276
    MDCN[30] 4 13.71 25.02/0.7128 23.84/0.5995 21.80/0.5885 23.82/0.5585 23.20/0.7251
    SwinIR[46] 4 11.90 25.03/0.7145 23.93/0.602 2 21.94/0.592 8 23.86/0.560 0 23.29/0.730 7
    DRCT [55] 4 13.48 24.95/0.7111 23.86/0.5988 21.79/0.5842 23.82/0.5580 23.16/0.7244
    HMA[56] 4 66.63 24.99/0.7120 23.87/0.5984 21.89/0.5881 23.83/0.5581 23.18/0.7254
    JCARSR 4 10.40 25.14/0.7174 24.03/0.6039 22.11/0.5967 23.92/0.5607 23.50/0.7338
    下载: 导出CSV

    表  2  JCARSR与JCAR-SR方法的定量比较

    Table  2  Quantitative comparison of JCARSR with JCAR-SR methods

    对比方法 尺度因子 Set5 Set14 Urban100 B100 Manga109
    PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM
    ARCNN[40] + SwinIR[46] 2 28.24/0.7988 26.34/0.6936 24.16/0.6947 25.84/0.6480 26.68/0.8184
    DnCNN3[41] + SwinIR[46] 2 28.54/0.8103 26.50/0.7017 24.40/0.7070 25.96/0.6542 27.01/0.8304
    QGCN[43] + SwinIR[46] 2 28.93/0.822 7 26.80/0.712 5 25.04/0.735 3 26.13/0.661 8 27.88/0.852 8
    FBCNN[44] + SwinIR[46] 2 28.87/0.8209 26.80/0.712 0 24.95/0.7311 26.13/0.6614 27.83/0.8527
    JDEC[12] + SwinIR[46] 2 27.76/0.7920 25.70/0.6678 23.63/0.6724 25.39/0.6236 25.61/0.8024
    JCARSR 2 29.13/0.8269 26.96/0.7158 25.28/0.7423 26.19/0.6637 28.37/0.8609
    ARCNN[40] + SwinIR[46] 4 24.60/0.6806 23.57/0.5826 21.41/0.5563 23.68/0.5485 22.55/0.6881
    DnCNN3[41] + SwinIR[46] 4 24.77/0.6911 23.70/0.5883 21.55/0.5640 23.74/0.5518 22.71/0.6965
    QGCN[43] + SwinIR[46] 4 25.04/0.708 5 23.93/0.5995 21.93/0.587 5 23.86/0.5577 23.17/0.7200
    FBCNN[44] + SwinIR[46] 4 25.02/0.7080 23.94/0.599 9 21.88/0.5851 23.87/0.557 8 23.20/0.723 6
    JDEC[12] + SwinIR[46] 4 24.58/0.6966 23.38/0.5786 21.10/0.5454 23.54/0.5437 21.82/0.6771
    JCARSR 4 25.14/0.7174 24.03/0.6039 22.11/0.5967 23.92/0.5607 23.50/0.7338
    下载: 导出CSV

    表  3  JCARSR与SR-JCAR方法的定量比较

    Table  3  Quantitative comparison of JCARSR with SR-JCAR methods

    对比方法 尺度因子 Set5 Set14 Urban100 B100 Manga109
    PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM
    SwinIR[46] + ARCNN[40] 2 27.23/0.759 2 25.59/0.671 7 23.38/0.6634 25.36/0.634 4 25.47/0.778 3
    SwinIR[46] + DnCNN3[41] 2 26.75/0.7355 25.23/0.6573 23.01/0.6442 25.13/0.6253 24.88/0.7507
    SwinIR[46] + QGCN[43] 2 26.97/0.7495 25.43/0.6661 23.25/0.6571 25.22/0.6293 25.30/0.7721
    SwinIR[46] + FBCNN[44] 2 27.14/0.7577 25.55/0.6717 23.38/0.664 2 25.30/0.6333 25.43/0.7781
    SwinIR[46] + JDEC[12] 2 27.25/0.7601 25.62/0.6701 23.46/0.6619 25.34/0.6315 25.71/0.7880
    JCARSR 2 29.13/0.8269 26.96/0.7158 25.28/0.7423 26.19/0.6637 28.37/0.8609
    SwinIR[46] + ARCNN[40] 4 23.46/0.6134 22.56/0.536 8 20.56/0.508 9 23.02/0.518 1 21.48/0.627 8
    SwinIR[46] + DnCNN3[41] 4 23.23/0.5936 22.40/0.5223 20.44/0.4956 22.86/0.5066 21.35/0.6079
    SwinIR[46] + QGCN[43] 4 23.45/0.6124 22.56/0.5350 20.57/0.5068 23.00/0.5172 21.50/0.6246
    SwinIR[46] + FBCNN[44] 4 23.46/0.613 8 22.58/0.5361 20.58/0.5079 23.01/0.518 1 21.51/0.6253
    SwinIR[46] + JDEC[12] 4 23.60/0.6170 22.71/0.5381 20.70/0.5102 23.09/0.5196 21.68/0.6286
    JCARSR 4 25.14/0.7174 24.03/0.6039 22.11/0.5967 23.92/0.5607 23.50/0.7338
    下载: 导出CSV

    表  4  不同JPEG压缩因子下的定量比较

    Table  4  Quantitative comparison under different JPEG compress factors

    对比方法 压缩因子 尺度因子 Set5 Set14 Urban100 B100 Manga109
    PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM
    SwinIR[46] 10 2 28.95/0.8231 26.87/0.7141 25.11/0.7377 26.13/0.6622 28.03/0.8565
    20 2 30.91/0.8638 28.31/0.7657 26.53/0.7911 27.33/0.7167 30.23/0.8943
    30 2 31.92/0.8822 29.11/0.7929 27.40/0.8205 27.98/0.7464 31.48/0.9120
    40 2 32.53/0.8926 29.63/0.8092 28.05/0.8385 28.40/0.7651 32.19/0.9214
    80 2 34.65/0.9230 31.48/0.8653 30.09/0.8881 30.19/0.8341 35.26/0.9497
    JCARSR 10 2 29.13/0.8269 26.96/0.7158 25.28/0.7423 26.19/0.6637 28.37/0.8609
    20 2 31.01/0.8660 28.41/0.7669 26.69/0.7943 27.38/0.7175 30.55/0.8972
    30 2 32.01/0.8836 29.18/0.7934 27.53/0.8215 28.02/0.7469 31.80/0.9137
    40 2 32.63/0.8939 29.69/0.8098 28.12/0.8386 28.47/0.7659 32.66/0.9235
    80 2 34.74/0.9233 31.50/0.8653 30.13/0.8883 30.20/0.8345 35.51/0.9502
    SwinIR[46] 10 4 25.03/0.7145 23.93/0.6022 21.94/0.5928 23.86/0.5600 23.29/0.7307
    20 4 26.50/0.7577 24.95/0.6369 22.88/0.6377 24.70/0.5917 24.77/0.7754
    30 4 27.48/0.7825 25.56/0.6572 23.42/0.6631 25.13/0.6095 25.59/0.7986
    40 4 28.04/0.7954 25.89/0.6695 23.78/0.6806 25.42/0.6216 26.14/0.8141
    80 4 29.89/0.8409 27.24/0.7229 25.02/0.7360 26.45/0.6733 28.33/0.8626
    JCARSR 10 4 25.14/0.7174 24.03/0.6039 22.11/0.5967 23.92/0.5607 23.50/0.7338
    20 4 26.66/0.7608 25.07/0.6387 22.99/0.6386 24.76/0.5925 24.94/0.7767
    30 4 27.54/0.7826 25.60/0.6578 23.51/0.6637 25.18/0.6104 25.78/0.8006
    40 4 28.07/0.7957 25.97/0.6715 23.87/0.6813 25.47/0.6234 26.39/0.8171
    80 4 29.94/0.8424 27.31/0.7246 25.06/0.7362 26.50/0.6745 28.41/0.8637
    下载: 导出CSV

    表  5  网络模块消融研究

    Table  5  Ablation study on the network block

    对比方法 Set5 Set14 Urban100 B100 Manga109
    PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM
    w/o MSNAFB 29.00/0.8232 26.84/0.7122 25.00/0.7311 26.12/0.6608 28.06/0.8545
    w/o MA 29.01/0.8237 26.85/0.7124 25.02/0.7316 26.13/0.6610 28.08/0.8545
    w/o MSCCB 29.02/0.8240 26.90/0.7136 25.11/0.7359 26.15/0.6618 28.19/0.8570
    JCARSR 29.13/0.8269 26.96/0.7158 25.28/0.7423 26.19/0.6637 28.37/0.8609
    下载: 导出CSV

    表  6  网络分支结构消融研究

    Table  6  Ablation study on the network branch structure

    对比方法 Set5 Set14 Urban100 B100 Manga109
    PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM
    ISRB 29.02/0.8242 26.89/0.7137 25.11/0.7366 26.15/0.6619 28.20/0.8575
    ISRB$ + $GGB 29.05/0.8242 26.90/0.7139 25.14/0.7373 26.15/0.6621 28.24/0.8578
    ISRB$ + $GGB$ + $CARB 29.13/0.8269 26.96/0.7158 25.28/0.7423 26.19/0.6637 28.37/0.8609
    下载: 导出CSV

    表  7  多分支融合方式消融研究

    Table  7  Ablation study on multi-branch fusion methods

    信息融合方式 Set5 Set14 Urban100 B100 Manga109
    PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM
    双向 29.04/0.8242 26.89/0.7136 25.10/0.7356 26.15/0.6618 28.19/0.8572
    单向 29.13/0.8269 26.96/0.7158 25.28/0.7423 26.19/0.6637 28.37/0.8609
    下载: 导出CSV

    表  8  梯度引导策略消融研究

    Table  8  Ablation study on gradient-guided strategy

    GL-ISRB GL-GGB Set5 Set14 Urban100 B100 Manga109
    PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM PSNR (dB)/SSIM
    × 29.06/0.8258 26.91/0.7160 25.20/0.7406 26.17/0.6645 28.27/0.8591
    × 29.13/0.8269 26.96/0.7158 25.28/0.7423 26.19/0.6637 28.37/0.8609
    29.11/0.8268 26.94/0.7161 25.25/0.7412 26.18/0.6642 28.31/0.8595
    下载: 导出CSV
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  • 收稿日期:  2024-07-22
  • 录用日期:  2025-04-22
  • 网络出版日期:  2025-05-14

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