《计算机应用》唯一官方网站 ›› 2022, Vol. 42 ›› Issue (1): 245-251.DOI: 10.11772/j.issn.1001-9081.2021010127
• 多媒体计算与计算机仿真 • 上一篇
李恒鑫1, 常侃1,2(), 谭宇飞1,3, 凌铭阳1, 覃团发1,2
收稿日期:
2021-01-22
修回日期:
2021-03-05
接受日期:
2021-03-17
发布日期:
2022-01-11
出版日期:
2022-01-10
通讯作者:
常侃
作者简介:
李恒鑫(1996—),男,江西抚州人,硕士研究生,主要研究方向:图像去马赛克、超分辨率Hengxin LI1, Kan CHANG1,2(), Yufei TAN1,3, Mingyang LING1, Tuanfa QIN1,2
Received:
2021-01-22
Revised:
2021-03-05
Accepted:
2021-03-17
Online:
2022-01-11
Published:
2022-01-10
Contact:
Kan CHANG
About author:
LI Hengxin, born in 1996, M. S. candidate. His research interests include color image demosaicking, super-resolution.Supported by:
摘要:
在商用数码相机中,由于CMOS传感器的限制,在采样得到的图像中的每个像素位置仅有一个色彩通道的信息,因此,需要采用彩色图像去马赛克(CDM)算法来恢复全彩图像。然而,现有的基于卷积神经网络(CNN)的CDM算法不能以较低的计算复杂度和网络参数量取得令人满意的性能。针对这个问题,提出一种应用通道间相关性和增强信息蒸馏(ICEID)的彩色图像去马赛克网络。首先,为了充分利用彩色图像的通道间相关性,提出了一种通道间的引导重建结构来生成初始CDM结果;其次,提出一种增强信息蒸馏模块(EIDM)来以相对较低的参数量有效地提取和精炼图像特征,从而高效地优化重建的全彩图像。实验结果表明,与主流CDM算法相比,所提算法不仅在客观质量与主观质量上均获得了明显提升,而且具有较低的计算复杂度和网络参数量。
中图分类号:
李恒鑫, 常侃, 谭宇飞, 凌铭阳, 覃团发. 应用通道间相关性及增强信息蒸馏的彩色图像去马赛克网络[J]. 计算机应用, 2022, 42(1): 245-251.
Hengxin LI, Kan CHANG, Yufei TAN, Mingyang LING, Tuanfa QIN. Color image demosaicking network based on inter-channel correlation and enhanced information distillation[J]. Journal of Computer Applications, 2022, 42(1): 245-251.
实验网络 | 参数量/103 | CPSNR/dB | SSIM |
---|---|---|---|
Base | 471 | 39.09 | 0.989 3 |
w/Bayer | 495 | 39.17 | 0.989 3 |
w/ID | 481 | 39.15 | 0.989 4 |
w/EID | 495 | 39.18 | 0.989 6 |
表1 在IMAX数据集上的消融实验
Tab. 1 Ablation study on IMAX dataset
实验网络 | 参数量/103 | CPSNR/dB | SSIM |
---|---|---|---|
Base | 471 | 39.09 | 0.989 3 |
w/Bayer | 495 | 39.17 | 0.989 3 |
w/ID | 481 | 39.15 | 0.989 4 |
w/EID | 495 | 39.18 | 0.989 6 |
图像 序号 | ARI[ | DJDD[ | DRL[ | 3-stage[ | ICEID | |||||
---|---|---|---|---|---|---|---|---|---|---|
CPSNR/dB | SSIM | CPSNR/dB | SSIM | CPSNR/dB | SSIM | CPSNR/dB | SSIM | CPSNR/dB | SSIM | |
平均值 | 37.46 | 0.964 3 | 37.76 | 0.986 6 | 38.73 | 0.988 2 | 38.78 | 0.988 3 | 39.18 | 0.989 5 |
1 | 29.70 | 0.923 6 | 30.03 | 0.968 6 | 31.12 | 0.972 6 | 31.00 | 0.971 9 | 31.34 | 0.974 3 |
2 | 39.20 | 0.972 1 | 35.44 | 0.980 4 | 35.88 | 0.981 3 | 35.97 | 0.981 9 | 36.38 | 0.983 9 |
3 | 40.11 | 0.973 3 | 34.97 | 0.989 6 | 35.97 | 0.991 8 | 36.14 | 0.991 8 | 36.81 | 0.992 3 |
4 | 39.91 | 0.964 7 | 37.86 | 0.996 1 | 40.21 | 0.997 1 | 40.16 | 0.997 0 | 40.63 | 0.997 3 |
5 | 40.68 | 0.951 0 | 35.49 | 0.985 1 | 36.71 | 0.987 8 | 36.42 | 0.987 2 | 36.77 | 0.988 4 |
6 | 38.91 | 0.956 2 | 38.69 | 0.990 2 | 40.64 | 0.992 8 | 40.61 | 0.992 7 | 41.07 | 0.993 9 |
7 | 39.14 | 0.958 4 | 40.81 | 0.992 5 | 40.78 | 0.992 6 | 41.30 | 0.992 9 | 41.81 | 0.993 3 |
8 | 35.53 | 0.966 9 | 40.58 | 0.990 5 | 41.06 | 0.991 2 | 41.28 | 0.991 5 | 41.36 | 0.991 7 |
9 | 34.65 | 0.961 7 | 38.65 | 0.988 3 | 39.65 | 0.989 5 | 39.74 | 0.989 8 | 40.03 | 0.990 8 |
10 | 36.34 | 0.966 1 | 39.65 | 0.990 6 | 40.61 | 0.991 6 | 40.63 | 0.991 7 | 41.06 | 0.992 7 |
11 | 35.19 | 0.945 4 | 40.67 | 0.989 9 | 41.50 | 0.990 9 | 41.53 | 0.990 8 | 41.78 | 0.991 4 |
12 | 34.84 | 0.972 8 | 40.07 | 0.988 3 | 40.99 | 0.988 8 | 40.95 | 0.989 1 | 41.49 | 0.991 1 |
13 | 38.19 | 0.989 5 | 41.34 | 0.986 1 | 41.80 | 0.986 5 | 41.94 | 0.987 6 | 42.10 | 0.990 0 |
14 | 35.59 | 0.962 9 | 39.58 | 0.987 6 | 39.86 | 0.987 3 | 39.96 | 0.987 6 | 40.34 | 0.989 5 |
15 | 39.45 | 0.972 3 | 39.70 | 0.984 2 | 40.10 | 0.984 9 | 40.15 | 0.985 0 | 40.54 | 0.986 5 |
16 | 39.66 | 0.978 8 | 35.36 | 0.986 1 | 36.79 | 0.988 9 | 36.44 | 0.988 3 | 37.26 | 0.989 7 |
17 | 39.53 | 0.978 6 | 34.12 | 0.978 8 | 35.85 | 0.985 8 | 36.07 | 0.986 2 | 36.53 | 0.987 6 |
18 | 37.63 | 0.963 0 | 36.69 | 0.985 3 | 37.57 | 0.986 6 | 37.68 | 0.986 7 | 38.05 | 0.987 5 |
表2 IMAX数据集上不同算法的定量比较
Tab. 2 Quantitative comparison of different algorithms on IMAX dataset
图像 序号 | ARI[ | DJDD[ | DRL[ | 3-stage[ | ICEID | |||||
---|---|---|---|---|---|---|---|---|---|---|
CPSNR/dB | SSIM | CPSNR/dB | SSIM | CPSNR/dB | SSIM | CPSNR/dB | SSIM | CPSNR/dB | SSIM | |
平均值 | 37.46 | 0.964 3 | 37.76 | 0.986 6 | 38.73 | 0.988 2 | 38.78 | 0.988 3 | 39.18 | 0.989 5 |
1 | 29.70 | 0.923 6 | 30.03 | 0.968 6 | 31.12 | 0.972 6 | 31.00 | 0.971 9 | 31.34 | 0.974 3 |
2 | 39.20 | 0.972 1 | 35.44 | 0.980 4 | 35.88 | 0.981 3 | 35.97 | 0.981 9 | 36.38 | 0.983 9 |
3 | 40.11 | 0.973 3 | 34.97 | 0.989 6 | 35.97 | 0.991 8 | 36.14 | 0.991 8 | 36.81 | 0.992 3 |
4 | 39.91 | 0.964 7 | 37.86 | 0.996 1 | 40.21 | 0.997 1 | 40.16 | 0.997 0 | 40.63 | 0.997 3 |
5 | 40.68 | 0.951 0 | 35.49 | 0.985 1 | 36.71 | 0.987 8 | 36.42 | 0.987 2 | 36.77 | 0.988 4 |
6 | 38.91 | 0.956 2 | 38.69 | 0.990 2 | 40.64 | 0.992 8 | 40.61 | 0.992 7 | 41.07 | 0.993 9 |
7 | 39.14 | 0.958 4 | 40.81 | 0.992 5 | 40.78 | 0.992 6 | 41.30 | 0.992 9 | 41.81 | 0.993 3 |
8 | 35.53 | 0.966 9 | 40.58 | 0.990 5 | 41.06 | 0.991 2 | 41.28 | 0.991 5 | 41.36 | 0.991 7 |
9 | 34.65 | 0.961 7 | 38.65 | 0.988 3 | 39.65 | 0.989 5 | 39.74 | 0.989 8 | 40.03 | 0.990 8 |
10 | 36.34 | 0.966 1 | 39.65 | 0.990 6 | 40.61 | 0.991 6 | 40.63 | 0.991 7 | 41.06 | 0.992 7 |
11 | 35.19 | 0.945 4 | 40.67 | 0.989 9 | 41.50 | 0.990 9 | 41.53 | 0.990 8 | 41.78 | 0.991 4 |
12 | 34.84 | 0.972 8 | 40.07 | 0.988 3 | 40.99 | 0.988 8 | 40.95 | 0.989 1 | 41.49 | 0.991 1 |
13 | 38.19 | 0.989 5 | 41.34 | 0.986 1 | 41.80 | 0.986 5 | 41.94 | 0.987 6 | 42.10 | 0.990 0 |
14 | 35.59 | 0.962 9 | 39.58 | 0.987 6 | 39.86 | 0.987 3 | 39.96 | 0.987 6 | 40.34 | 0.989 5 |
15 | 39.45 | 0.972 3 | 39.70 | 0.984 2 | 40.10 | 0.984 9 | 40.15 | 0.985 0 | 40.54 | 0.986 5 |
16 | 39.66 | 0.978 8 | 35.36 | 0.986 1 | 36.79 | 0.988 9 | 36.44 | 0.988 3 | 37.26 | 0.989 7 |
17 | 39.53 | 0.978 6 | 34.12 | 0.978 8 | 35.85 | 0.985 8 | 36.07 | 0.986 2 | 36.53 | 0.987 6 |
18 | 37.63 | 0.963 0 | 36.69 | 0.985 3 | 37.57 | 0.986 6 | 37.68 | 0.986 7 | 38.05 | 0.987 5 |
图像 序号 | ARI[ | DJDD[ | DRL[ | 3-stage[ | ICEID | |||||
---|---|---|---|---|---|---|---|---|---|---|
CPSNR/dB | SSIM | CPSNR/dB | SSIM | CPSNR/dB | SSIM | CPSNR/dB | SSIM | CPSNR/dB | SSIM | |
平均值 | 39.30 | 0.981 6 | 41.31 | 0.994 9 | 41.95 | 0.995 6 | 42.17 | 0.995 5 | 42.67 | 0.996 2 |
1 | 38.80 | 0.986 9 | 40.87 | 0.996 3 | 41.58 | 0.996 9 | 41.90 | 0.997 0 | 43.01 | 0.997 7 |
2 | 36.68 | 0.970 5 | 41.07 | 0.990 8 | 41.62 | 0.992 8 | 41.81 | 0.992 8 | 42.20 | 0.993 8 |
3 | 42.46 | 0.987 8 | 43.81 | 0.996 2 | 44.99 | 0.997 0 | 45.11 | 0.996 7 | 45.43 | 0.997 2 |
4 | 39.06 | 0.980 6 | 42.10 | 0.994 0 | 43.18 | 0.995 2 | 43.38 | 0.994 9 | 43.96 | 0.995 6 |
5 | 38.27 | 0.989 0 | 39.67 | 0.996 8 | 40.78 | 0.997 5 | 40.90 | 0.997 4 | 41.38 | 0.997 8 |
6 | 40.10 | 0.988 2 | 41.46 | 0.996 3 | 42.15 | 0.997 0 | 42.37 | 0.996 9 | 43.11 | 0.997 4 |
7 | 42.04 | 0.988 6 | 43.50 | 0.996 7 | 44.61 | 0.997 3 | 44.72 | 0.997 1 | 44.95 | 0.997 5 |
8 | 35.43 | 0.981 6 | 38.15 | 0.995 2 | 38.77 | 0.995 8 | 39.08 | 0.995 9 | 39.94 | 0.996 6 |
9 | 41.87 | 0.969 4 | 44.14 | 0.994 7 | 44.39 | 0.995 2 | 44.66 | 0.994 9 | 44.99 | 0.995 4 |
10 | 41.94 | 0.981 7 | 43.54 | 0.995 1 | 43.99 | 0.995 6 | 44.21 | 0.995 4 | 44.47 | 0.995 9 |
11 | 39.68 | 0.984 4 | 41.66 | 0.995 2 | 42.20 | 0.995 9 | 42.42 | 0.995 9 | 42.92 | 0.996 6 |
12 | 42.95 | 0.986 8 | 44.27 | 0.995 8 | 44.84 | 0.996 4 | 45.18 | 0.996 2 | 45.54 | 0.996 8 |
13 | 35.22 | 0.981 5 | 36.96 | 0.994 6 | 37.18 | 0.995 1 | 37.50 | 0.995 3 | 38.43 | 0.996 4 |
14 | 37.87 | 0.984 8 | 39.20 | 0.995 0 | 40.10 | 0.996 1 | 40.19 | 0.996 0 | 40.84 | 0.996 7 |
15 | 36.52 | 0.969 6 | 41.01 | 0.993 5 | 41.24 | 0.994 1 | 41.44 | 0.994 2 | 41.97 | 0.995 1 |
16 | 43.40 | 0.989 3 | 44.96 | 0.996 7 | 45.60 | 0.997 1 | 45.76 | 0.996 9 | 46.15 | 0.997 4 |
17 | 41.63 | 0.987 1 | 42.62 | 0.995 5 | 43.01 | 0.996 0 | 43.23 | 0.995 8 | 43.60 | 0.996 2 |
18 | 36.83 | 0.976 3 | 38.35 | 0.993 3 | 38.64 | 0.993 8 | 38.94 | 0.993 7 | 39.56 | 0.994 5 |
19 | 40.74 | 0.983 1 | 42.01 | 0.994 7 | 42.33 | 0.995 2 | 42.77 | 0.995 1 | 43.49 | 0.995 7 |
20 | 35.55 | 0.976 3 | 42.03 | 0.994 9 | 42.66 | 0.996 0 | 42.96 | 0.995 6 | 43.03 | 0.996 0 |
21 | 39.33 | 0.973 2 | 41.16 | 0.994 8 | 41.50 | 0.995 3 | 41.70 | 0.995 1 | 42.30 | 0.995 7 |
22 | 38.32 | 0.973 7 | 39.51 | 0.992 1 | 40.39 | 0.993 0 | 40.47 | 0.992 9 | 40.80 | 0.993 8 |
23 | 43.27 | 0.981 8 | 42.88 | 0.994 5 | 44.38 | 0.995 5 | 44.32 | 0.995 2 | 43.93 | 0.995 9 |
24 | 35.35 | 0.985 2 | 36.40 | 0.995 1 | 36.68 | 0.995 4 | 36.96 | 0.995 3 | 38.07 | 0.996 4 |
表3 Kodak数据集上不同算法的定量比较
Tab. 3 Quantitative comparison of different algorithms on Kodak dataset
图像 序号 | ARI[ | DJDD[ | DRL[ | 3-stage[ | ICEID | |||||
---|---|---|---|---|---|---|---|---|---|---|
CPSNR/dB | SSIM | CPSNR/dB | SSIM | CPSNR/dB | SSIM | CPSNR/dB | SSIM | CPSNR/dB | SSIM | |
平均值 | 39.30 | 0.981 6 | 41.31 | 0.994 9 | 41.95 | 0.995 6 | 42.17 | 0.995 5 | 42.67 | 0.996 2 |
1 | 38.80 | 0.986 9 | 40.87 | 0.996 3 | 41.58 | 0.996 9 | 41.90 | 0.997 0 | 43.01 | 0.997 7 |
2 | 36.68 | 0.970 5 | 41.07 | 0.990 8 | 41.62 | 0.992 8 | 41.81 | 0.992 8 | 42.20 | 0.993 8 |
3 | 42.46 | 0.987 8 | 43.81 | 0.996 2 | 44.99 | 0.997 0 | 45.11 | 0.996 7 | 45.43 | 0.997 2 |
4 | 39.06 | 0.980 6 | 42.10 | 0.994 0 | 43.18 | 0.995 2 | 43.38 | 0.994 9 | 43.96 | 0.995 6 |
5 | 38.27 | 0.989 0 | 39.67 | 0.996 8 | 40.78 | 0.997 5 | 40.90 | 0.997 4 | 41.38 | 0.997 8 |
6 | 40.10 | 0.988 2 | 41.46 | 0.996 3 | 42.15 | 0.997 0 | 42.37 | 0.996 9 | 43.11 | 0.997 4 |
7 | 42.04 | 0.988 6 | 43.50 | 0.996 7 | 44.61 | 0.997 3 | 44.72 | 0.997 1 | 44.95 | 0.997 5 |
8 | 35.43 | 0.981 6 | 38.15 | 0.995 2 | 38.77 | 0.995 8 | 39.08 | 0.995 9 | 39.94 | 0.996 6 |
9 | 41.87 | 0.969 4 | 44.14 | 0.994 7 | 44.39 | 0.995 2 | 44.66 | 0.994 9 | 44.99 | 0.995 4 |
10 | 41.94 | 0.981 7 | 43.54 | 0.995 1 | 43.99 | 0.995 6 | 44.21 | 0.995 4 | 44.47 | 0.995 9 |
11 | 39.68 | 0.984 4 | 41.66 | 0.995 2 | 42.20 | 0.995 9 | 42.42 | 0.995 9 | 42.92 | 0.996 6 |
12 | 42.95 | 0.986 8 | 44.27 | 0.995 8 | 44.84 | 0.996 4 | 45.18 | 0.996 2 | 45.54 | 0.996 8 |
13 | 35.22 | 0.981 5 | 36.96 | 0.994 6 | 37.18 | 0.995 1 | 37.50 | 0.995 3 | 38.43 | 0.996 4 |
14 | 37.87 | 0.984 8 | 39.20 | 0.995 0 | 40.10 | 0.996 1 | 40.19 | 0.996 0 | 40.84 | 0.996 7 |
15 | 36.52 | 0.969 6 | 41.01 | 0.993 5 | 41.24 | 0.994 1 | 41.44 | 0.994 2 | 41.97 | 0.995 1 |
16 | 43.40 | 0.989 3 | 44.96 | 0.996 7 | 45.60 | 0.997 1 | 45.76 | 0.996 9 | 46.15 | 0.997 4 |
17 | 41.63 | 0.987 1 | 42.62 | 0.995 5 | 43.01 | 0.996 0 | 43.23 | 0.995 8 | 43.60 | 0.996 2 |
18 | 36.83 | 0.976 3 | 38.35 | 0.993 3 | 38.64 | 0.993 8 | 38.94 | 0.993 7 | 39.56 | 0.994 5 |
19 | 40.74 | 0.983 1 | 42.01 | 0.994 7 | 42.33 | 0.995 2 | 42.77 | 0.995 1 | 43.49 | 0.995 7 |
20 | 35.55 | 0.976 3 | 42.03 | 0.994 9 | 42.66 | 0.996 0 | 42.96 | 0.995 6 | 43.03 | 0.996 0 |
21 | 39.33 | 0.973 2 | 41.16 | 0.994 8 | 41.50 | 0.995 3 | 41.70 | 0.995 1 | 42.30 | 0.995 7 |
22 | 38.32 | 0.973 7 | 39.51 | 0.992 1 | 40.39 | 0.993 0 | 40.47 | 0.992 9 | 40.80 | 0.993 8 |
23 | 43.27 | 0.981 8 | 42.88 | 0.994 5 | 44.38 | 0.995 5 | 44.32 | 0.995 2 | 43.93 | 0.995 9 |
24 | 35.35 | 0.985 2 | 36.40 | 0.995 1 | 36.68 | 0.995 4 | 36.96 | 0.995 3 | 38.07 | 0.996 4 |
算法 | 参数量/103 | FLOPs/109 |
---|---|---|
ARI[ | — | — |
DJDD[ | 561 | 264 |
DRL[ | 229 | 423 |
3-stage[ | 1 793 | 3 309 |
本文算法 | 495 | 227 |
表4 不同算法的参数量和FLOPs
Tab. 4 Parameter number and FLOPs of different algorithms
算法 | 参数量/103 | FLOPs/109 |
---|---|---|
ARI[ | — | — |
DJDD[ | 561 | 264 |
DRL[ | 229 | 423 |
3-stage[ | 1 793 | 3 309 |
本文算法 | 495 | 227 |
1 | BAYER B E. Color imaging array: US, 3971065[P]. 1976-07-20. |
2 | LONGERE P, ZHANG X M, DELAHUNT P B, et al. Perceptual assessment of demosaicing algorithm performance[J]. Proceedings of the IEEE, 2002, 90(1): 123-132. 10.1109/5.982410 |
3 | ARÀNDIGA F. A nonlinear algorithm for monotone piecewise bicubic interpolation[J]. Applied Mathematics and Computation, 2016, 272(Pt 1): 100-113. 10.1016/j.amc.2015.08.027 |
4 | McKINLEY S, LEVINE M. Cubic spline interpolation[EB/OL]. [2020-12-03].. |
5 | KIKU D, MONNO Y, TANAKA M, et al. Residual interpolation for color image demosaicking[C]// Proceedings of the 2013 IEEE International Conference on Image Processing. Piscataway: IEEE, 2013: 2304-2308. 10.1109/icip.2013.6738475 |
6 | KIKU D, MONNO Y, TANAKA M, et al. Minimized-Laplacian residual interpolation for color image demosaicking[C]// Proceedings of the SPIE 9023, Digital Photography X. Bellingham, WA: SPIE, 2014: No.90230L. 10.1117/12.2038425 |
7 | MONNO Y, KIKU D, TANAKA M, et al. Adaptive residual interpolation for color image demosaicking[C]// Proceedings of the 2015 IEEE International Conference on Image Processing. Piscataway: IEEE, 2015: 3861-3865. 10.1109/icip.2015.7351528 |
8 | 黄丽丽,肖亮,韦志辉. 彩色图像去马赛克的非局部稀疏表示方法[J]. 电子学报, 2014, 42(2): 272-279. 10.3969/j.issn.0372-2112.2014.02.010 |
HUANG L L, XIAO L, WEI Z H. A nonlocal sparse representation method for color demosaicking[J]. Acta Electronica Sinica, 2014, 42(2): 272-279. 10.3969/j.issn.0372-2112.2014.02.010 | |
9 | ZHANG L, WU X L, BUADES A, et al. Color demosaicking by local directional interpolation and nonlocal adaptive thresholding[J]. Journal of Electronic Imaging, 2011, 20(2): No.023016. 10.1117/1.3600632 |
10 | CHANG K, DING P L K, LI B X. Color image demosaicking using inter-channel correlation and nonlocal self-similarity[J]. Signal Processing: Image Communication, 2015, 39(Pt A): 264-279. 10.1016/j.image.2015.10.003 |
11 | GHARBI M, CHAURASIA G, PARIS S, et al. Deep joint demosaicking and denoising[J]. ACM Transactions on Graphics, 2016, 35(6): No.191. 10.1145/2980179.2982399 |
12 | TAN R J, ZHANG K, ZUO W M, et al. Color image demosaicking via deep residual learning[EB/OL]. [2020-12-03].. |
13 | TAN D S, CHEN W Y, HUA K L. DeepDemosaicking: adaptive image demosaicking via multiple deep fully convolutional networks[J]. IEEE Transactions on Image Processing, 2018, 27(5): 2408-2419. 10.1109/tip.2018.2803341 |
14 | CUI K, JIN Z, STEINBACH E. Color image demosaicking using a 3-stage convolutional neural network structure[C]// Proceedings of the 25th IEEE International Conference on Image Processing. Piscataway: IEEE, 2018: 2177-2181. 10.1109/icip.2018.8451020 |
15 | 谢长江,杨晓敏,严斌宇,等. 基于深度学习的彩色以及近红外图像去马赛克[J]. 计算机应用, 2019, 39(10): 2899-2904. |
XIE C J, YANG X M, YAN B Y, et al. RGB-NIR image demosaicing based on deep learning[J]. Journal of Computer Applications, 2019, 39(10): 2899-2904. | |
16 | CHANG K, LI M H, DING P L K, et al. Accurate single image super-resolution using multi-path wide-activated residual network[J]. Signal Processing, 2020, 172: No.107567. 10.1016/j.sigpro.2020.107567 |
17 | HUI Z, WANG X M, GAO X B. Fast and accurate single image super-resolution via information distillation network[C]// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 723-731. 10.1109/cvpr.2018.00082 |
18 | SHI W Z, CABALLERO J, HUSZÁR F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 1874-1883. 10.1109/cvpr.2016.207 |
19 | ZHANG Y L, LI K P, LI K, et al. Image super-resolution using very deep residual channel attention networks[C]// Proceedings of the 2018 European Conference on Computer Vision, LNCS11211. Cham: Springer, 2018: 294-310. 10.1007/978-3-030-01234-2_18 |
20 | AGUSTSSON E, TIMOFTE R. NTIRE 2017 Challenge on single image super-resolution: dataset and study[C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Piscataway: IEEE, 2017: 1122-1131. 10.1109/cvprw.2017.150 |
[1] | 王素玉, 杨静, 李越. 基于双注意力机制信息蒸馏网络的图像超分辨率复原算法[J]. 《计算机应用》唯一官方网站, 2022, 42(1): 239-244. |
[2] | 宋中山, 梁家锐, 郑禄, 刘振宇, 帖军. 基于双向门控尺度特征融合的遥感场景分类[J]. 计算机应用, 2021, 41(9): 2726-2735. |
[3] | 卞凌志, 王直杰. 基于增强多维多粒度级联森林的信用评分模型[J]. 计算机应用, 2021, 41(9): 2539-2544. |
[4] | 李康康, 张静. 基于注意力机制的多层次编码和解码的图像描述模型[J]. 计算机应用, 2021, 41(9): 2504-2509. |
[5] | 张永斌, 常文欣, 孙连山, 张航. 基于字典的域名生成算法生成域名的检测方法[J]. 计算机应用, 2021, 41(9): 2609-2614. |
[6] | 赵宏, 孔东一. 图像特征注意力与自适应注意力融合的图像内容中文描述[J]. 计算机应用, 2021, 41(9): 2496-2503. |
[7] | 徐江浪, 李林燕, 万新军, 胡伏原. 结合目标检测的室内场景识别方法[J]. 计算机应用, 2021, 41(9): 2720-2725. |
[8] | 牟长宁, 王海鹏, 周丕宇, 侯鑫行. 基于图卷积神经网络的串联质谱从头测序[J]. 计算机应用, 2021, 41(9): 2773-2779. |
[9] | 王贺兵, 张春梅. 基于非对称卷积-压缩激发-次代残差网络的人脸关键点检测[J]. 计算机应用, 2021, 41(9): 2741-2747. |
[10] | 曹玉红, 徐海, 刘荪傲, 王紫霄, 李宏亮. 基于深度学习的医学影像分割研究综述[J]. 《计算机应用》唯一官方网站, 2021, 41(8): 2273-2287. |
[11] | 秦斌斌, 彭良康, 卢向明, 钱江波. 司机分心驾驶检测研究进展[J]. 计算机应用, 2021, 41(8): 2330-2337. |
[12] | 黄程程, 董霄霄, 李钊. 基于二维Winograd算法的深流水线5×5卷积方法[J]. 计算机应用, 2021, 41(8): 2258-2264. |
[13] | 曾祥银, 郑伯川, 刘丹. 基于深度卷积神经网络和聚类的左右轨道线检测[J]. 计算机应用, 2021, 41(8): 2324-2329. |
[14] | 吴则举, 焦翠娟, 陈亮. 基于改进Faster R-CNN的轮胎缺陷检测方法[J]. 计算机应用, 2021, 41(7): 1939-1946. |
[15] | 杨粟, 欧阳智, 杜逆索. 基于相关度距离的无监督并行哈希图像检索[J]. 计算机应用, 2021, 41(7): 1902-1907. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||