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), The 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 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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References(41)

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