开普勒定律启发的单幅图像细节增强算法

江鹤; 孙蟒; 郑州; 吴沛霖; 程德强; 周晨

doi:10.11999/JEIT250455

开普勒定律启发的单幅图像细节增强算法

doi: 10.11999/JEIT250455 cstr: 32379.14.JEIT250455

江鹤¹,
孙蟒¹,
郑州¹,
吴沛霖¹,
程德强¹,
周晨^1, ,

中国矿业大学信息与控制工程学院徐州 221116

基金项目: 国家自然科学基金(52304182, 52204177)，国家重点研发计划(2023YFC2907600, 2021YFC2902701, 2021YFC2902702)

详细信息

作者简介:
江鹤：男，讲师，研究方向为图像处理

孙蟒：男，硕士生，研究方向为图像细节增强

郑州：男，硕士生，研究方向为图像细节增强

吴沛霖：男，研究方向为图像细节增强

程德强：男，教授，研究方向为图像处理

周晨：女，讲师，研究方向为图像识别

通讯作者:
周晨　zc111@cumt.edu.cn

中图分类号: TN713; TP391
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- 文章访问数: 83
- HTML全文浏览量: 42
- PDF下载量: 17
- 被引次数: 0
出版历程
- 收稿日期: 2025-05-26
- 修回日期: 2025-11-03
- 录用日期: 2025-11-03
- 网络出版日期: 2025-11-12

Kepler’s Laws Inspired Single Image Detail Enhancement Algorithm

School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China

Funds: The National Natural Science Foundation of China (52304182, 52204177), The National Key Research and Development Program of China (2023YFC2907600, 2021YFC2902701, 2021YFC2902702)

摘要

摘要: 近年来，基于残差学习的单幅图像细节增强算法备受关注，其通过更新残差层来拟合图像细节层，并与原图像线性叠加实现图像细节增强。然而，此更新过程使用的方法是贪心算法，极易使系统陷入局部最优解，进而限制了系统性能。鉴于此，受行星运动规律的启发，该研究将残差更新类比为行星空间位置的动态调整，借鉴开普勒定律，通过计算确定行星的全局最优位置，进而实现残差层的精准更新。具体而言，将输入图像分块，对每个原始图像块，将其候选图像块视为“行星”，最佳匹配块视为“恒星”。通过计算每个“行星”与原始图像块之间的差异、“行星”的速度和“恒星”的引力，更新“行星”和“恒星”的位置，直至“恒星”的位置达到收敛状态，确定全局最佳匹配块的位置。实验结果显示，该研究提出的算法在视觉效果及量化评估方面均优于当前方法。值得一提的是，在BSDS200数据集4倍增强因子的结果中，该研究提出的方法比当前流行方法QWLS的量化指标PSNR和SSIM分别高出1.51 dB和0.041 3，彰显了该研究算法的优越性。
- 单幅图像细节增强 /
- 残差学习 /
- 开普勒定律 /
- 全局最优解
Abstract: Objective Single-image detail enhancement based on residual learning has received extensive attention in recent years. In these methods, the residual layer is updated by using the similarity between the residual layer and the detail layer, and it is then combined linearly with the original image to enhance image detail. This update process is a greedy algorithm, which tends to trap the system in local optima and limits overall performance. Inspired by Kepler’s laws, the residual update is treated as the dynamic adjustment of planetary positions. By applying Kepler’s laws and computing the global optimal position of the planets, precise updates of the residual layer are achieved. Methods The input image is partitioned into multiple blocks. For each block, its candidate blocks are treated as “planets”, and the best matching block is treated as a “star”. The positions of the “planets” and the “star” are updated by computing the differences between each “planet” and the original image block until the positions converge, which determines the location of the global optimal matching block. Results and Discussions In this study, 16 algorithms are tested on three datasets at two magnification levels (Table 1). The test results show that the proposed algorithm achieves strong performance in both PSNR and SSIM evaluations. During detail enhancement, compared with other algorithms, the proposed algorithm shows stronger edge preservation capability (Fig. 7). However, it is not robust to noise (Fig. 8–Fig. 10), and the performance of the enhanced images continues to decline as noise intensity increases (Fig.11). Both the initial gravitational constant and the gravitational attenuation rate constant present a fluctuating trend, meaning they increase first and then decrease (Fig. 12). When the gradient loss and texture loss weights are set to 0.001, the KLDE system achieves its best performance (Fig. 13). Conclusions This study proposes a single-image detail enhancement algorithm inspired by Kepler’s laws. By treating the residual update process as the dynamic adjustment of planetary positions, the algorithm applies Kepler’s laws to optimize residual layer updates, reduces the tendency of greedy search to reach local optima, and achieves more precise image detail enhancement. Experimental results show that the algorithm performs better than existing methods in visual effects and quantitative metrics and produces natural enhancement results. The running time remains relatively long because the iterative update of candidate blocks and the calculation of parameters such as gravity form the main computational bottleneck. Future work will focus on optimizing the algorithm structure to reduce unnecessary searches and improve system efficiency. The algorithm does not require training and achieves strong performance, which indicates potential value in high-precision offline image enhancement scenarios.
- Single image detail enhancement /
- Residual learning /
- Kepler’s laws /
- Global optimal solution

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图 1 研究动机原理图

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图 2 基于开普勒优化的单幅细节增强算法框架

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图 3 开普勒优化算法原理图

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图 4 RealSRSet中pattern图像的4倍细节放大对比示意图

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图 5 BSDS200中28075图像的4倍细节放大对比示意图

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图 6 BSDS200中87065图像的4倍细节放大对比示意图

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图 7 强度曲线对比图

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图 8 RealSRSet中ppt3高斯噪声图像4倍细节放大对比图

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图 9 RealSRSet中dog椒盐噪声图像4倍细节放大对比图

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图 11 不同噪声类型和强度下图像细节增强的定量指标曲线图

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图 10 RealSRSet中butterfly2泊松噪声4倍细节放大对比图

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图 12 引力初值与引力衰减速率对PSNR影响的对比图

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图 13 损失函数参数$ \zeta $和$ \xi $的消融实验图

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表 1 增强因子为2和4时在基准数据集下的指标对比

模型	增强因子	RealSRSet^[32]		BSDS200^[33]		T91^[34]
模型	增强因子	PSNR(dB)	SSIM	PSNR(dB)	SSIM	PSNR(dB)	SSIM
WLS(TOG 2008)^[15]	X2	20.16	0.8235	17.74	0.7439	18.63	0.7693
GIF(TPAMI 2012)^[8]		24.45	0.8908	23.89	0.8344	23.82	0.8444
WGIF(TIP 2015)^[9]		25.66	0.8867	27.91	0.8816	24.85	0.8517
GGIF(TIP 2015)^[10]		27.35	0.9256	27.41	0.8865	26.95	0.8955
ZF(ICCV 2017)^[30]		20.65	0.7731	22.56	0.7966	24.09	0.9237
BFLS(TCSVT 2018)^[36]		22.51	0.8318	23.06	0.7965	21.63	0.7659
ILS(TOG 2020)^[17]		26.16	0.8761	25.09	0.8313	23.48	0.8093
DIP(IJCV 2020)^[38]		23.00	0.7970	23.75	0.7657	25.87	0.8232
IPRH(JEI 2020)^[31]		26.30	0.9147	24.97	0.8629	28.48	0.9102
TH(TPAMI 2021)^[37]		20.70	0.7872	21.32	0.7620	21.43	0.7616
DeepFSPIS(ACM MM 2022)^[39]		26.13	0.8640	27.05	0.8640	25.67	0.8260
CSGIS(CGF 2022)^[40]		24.50	0.8340	25.32	0.8355	25.18	0.8216
PTF(TOG 2023)^[35]		18.43	0.7365	19.12	0.7116	18.76	0.6939
MGPNet(SPL 2024)^[41]		20.56	0.6953	23.38	0.8430	21.18	0.7489
QWLS(IJCV 2024)^[18]		24.54	0.8894	26.83	0.9099	27.50	0.9153
KLDE(本研究)		31.20	0.9718	28.59	0.9344	32.32	0.9626
WLS(TOG 2008)^[15]	X4	-	-	-	-	-	-
GIF(TPAMI 2012)^[8]		19.53	0.7638	18.71	0.6637	18.73	0.6862
WGIF(TIP 2015)^[9]		20.87	0.7781	22.54	0.7517	19.77	0.7081
GGIF(TIP 2015)^[10]		22.09	0.8294	21.90	0.7517	21.53	0.7699
ZF(ICCV 2017)^[30]		16.60	0.6038	18.07	0.6218	19.36	0.6081
BFLS(TCSVT 2018)^[36]		18.70	0.6982	18.07	0.6174	16.95	0.5898
ILS(TOG 2020)^[17]		21.10	0.7491	19.81	0.6636	18.49	0.6432
DIP(IJCV 2020)^[38]		19.69	0.6988	21.16	0.6860	22.71	0.7285
IPRH(JEI 2020)^[31]		21.47	0.8039	20.14	0.7105	23.30	0.7782
TH(TPAMI 2021)^[37]		16.72	0.6354	16.77	0.5786	16.91	0.5822
DeepFSPIS(ACM MM 2022)^[39]		20.88	0.7220	21.59	0.7030	20.47	0.6510
CSGIS(CGF 2022)^[40]		19.16	0.6651	19.82	0.6523	19.77	0.6344
PTF(TOG 2023)^[35]		15.09	0.5777	15.17	0.5244	14.89	0.5080
MGPNet(SPL 2024)^[41]		16.86	0.5422	19.47	0.7070	18.02	0.6018
QWLS(IJCV 2024)^[18]		20.27	0.7721	21.83	0.7906	22.88	0.7950
KLDE(本研究)		25.87	0.9181	23.34	0.8319	26.84	0.8921
注：黑色加粗字体为每列最优值，加下划线字体为每列次优值

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表 2 MOS比较

数据集	增强因子	排名前4的算法
RealSRSet^[32]	X2	KLDE>QWLS^[18]>IPRH^[31]>ZF^[30]
BSDS200^[33]		QWLS^[18]>KLDE>IPRH^[31]>PTF^[35]
T91^[34]		KLDE>QWLS^[18]>ZF^[30]>IPRH^[31]
RealSRSet^[32]	X4	KLDE>QWLS^[18]>IPRH^[31]]>ZF^[30]
BSDS200^[33]		KLDE>QWLS^[18]]>IPRH^[31]]>PTF^[35]
T91^[34]		KLDE>QWLS^[18]>ZF^[27]>IPRH^[31]
注：黑色加粗字体为本研究算法

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表 3 数据集RealSRSet上增强因子为4时运行时间比较

模型	平均运行时间(s)	PSNR(dB)
GIF(TPAMI 2012)^[8]	0.04	19.53
WGIF(TIP 2015)^[9]	0.08	20.87
GGIF(TIP 2015)^[10]	0.08	22.09
ZF(ICCV 2017)^[30]	0.04	16.60
BFLS(TCSVT 2018)^[36]	0.52	18.70
ILS(TOG 2020)^[17]	0.44	21.10
DIP(IJCV 2020)^[38]	4.01	19.69
IPRH(JEI 2020)^[31]	0.03	21.47
TH(TPAMI 2021)^[37]	0.39	16.72
DeepFSPIS(ACM MM 2022)^[39]	0.52	20.88
CSGIS(CGF 2022)^[40]	0.16	19.16
PTF(TOG 2023)^[35]	7.64	15.09
MGPNet(SPL 2024)^[41]	0.21	16.86
QWLS(IJCV 2024)^[18]	0.39	20.27
KLDE(本研究)	13.29	25.87
注：黑色加粗字体为本研究算法

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