Kepler’s Laws Inspired Single Image Detail Enhancement Algorithm

JIANG He; SUN Mang; ZHENG Zhou; WU Peilin; CHENG Deqiang; ZHOU Chen

doi:10.11999/JEIT250455

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JIANG He, SUN Mang, ZHENG Zhou, WU Peilin, CHENG Deqiang, ZHOU Chen. Kepler’s Laws Inspired Single Image Detail Enhancement Algorithm[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250455

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JIANG He, SUN Mang, ZHENG Zhou, WU Peilin, CHENG Deqiang, ZHOU Chen. Kepler’s Laws Inspired Single Image Detail Enhancement Algorithm[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250455

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Kepler’s Laws Inspired Single Image Detail Enhancement Algorithm

doi: 10.11999/JEIT250455 cstr: 32379.14.JEIT250455

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), National Key Research and Development Program of China (2023YFC2907600, 2021YFC2902701, 2021YFC2902702)

Received Date: 2025-05-26
Accepted Date: 2025-11-03
Rev Recd Date: 2025-11-03

Available Online: 2025-11-12

Abstract

Abstract

Objective In recent years, single-image detail enhancement based on residual learning have attracted extensive attention. These algorithms update the residual layer by leveraging the similarity between the residual layer and the detail layer, and then linearly combine it with the original image to enhance image details. However, this update process is a greedy algorithm, which is prone to trapping the system in local optima, thereby limiting the overall performance. Inspired by Kepler’s laws, this study analogizes the residual update to the dynamic adjustment of planetary positions. By applying Kepler's laws and calculating the global optimal position of the planets, precise updates of the residual layer are achieved. Methods The input image is divided into multiple blocks. For each block, its candidate blocks are regarded as “lanets”, and the best matching block is treated as a “star”. The positions of the “planets” and the “star” are updated by calculating the differences between each “planet” and the original image block until the positions converge, thereby determining the location of the global optimal matching block. Results and Discussions In this study, 16 algorithms are tested on three datasets at two different magnification levels (Table 1). The test results show that the proposed algorithm performs excellently in both PSNR and SSIM evaluations. During the detail enhancement process, compared with other algorithms, the proposed algorithm demonstrates stronger edge preservation capabilities (Fig.7). However, the algorithm proposed in this paper is not robust to noise (Fig.8-Fig.10), and the performance of the detail-enhanced images continues to decline as the noise intensity increases (Fig.11). Both the initial gravitational constant and the gravitational attenuation rate constant show a fluctuating trend, that is, they first increase and then decrease (Fig.12). When the gradient loss and texture loss weights are set to 0.001, the KLDE system achieves the best performance (Fig.13). Conclusions This study proposes a single-image detail enhancement algorithm inspired by Keple’s laws. By analogizing the residual update process to the dynamic adjustment of planetary positions, the algorithm utilizes Kepler's laws to optimize the update of residual layers, alleviating the local optimum problem caused by greedy search and achieving more precise image detail enhancement. Experimental results show that this algorithm outperforms existing methods in both visual effects and quantitative metrics, and can achieve natural enhancement performance. However, it is worth noting that the algorithm has a relatively long running time, with the computational bottleneck being limited by the iterative update of candidate blocks and the calculation of parameters such as gravity. Future work will focus on optimizing the algorithm structure to reduce invalid searches and improve system operation efficiency. Additionally, this algorithm does not require training and has good performance, and it shows potential and promotion value in scenarios such as high-precision offline image enhancement.
- Single image detail enhancement,
- Residual learning,
- Kepler's laws,
- Global optimal solution

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