<![CDATA[Astronomical images often suffer from low contrast, uneven illumination, dark backgrounds, and sensor-induced noise, which can obscure faint celestial structures and reduce the effectiveness of subsequent image analysis. Conventional homomorphic filtering can improve illumination and contrast by attenuating low-frequency components and enhancing high-frequency details, but its use of a fixed cutoff frequency limits its adaptability to images with different spectral characteristics. This study proposes a Spectral Energy-Based Adaptive Homomorphic Filtering (SEAHF) method that estimates the cutoff frequency from the spectral energy ratio between low- and high-frequency components of each input image. The proposed mechanism modifies only the cutoff frequency while retaining the conventional Gaussian homomorphic filtering framework and fixed remaining filter parameters. The method was evaluated on 60 astronomical images from the SDSS, DECaLS, and Pan-STARRS surveys, selected from common sky coordinates available across the three sources, and compared with Contrast Limited Adaptive Histogram Equalization (CLAHE), Conventional Homomorphic Filtering (CHF), and Low-Light Image Enhancement via Illumination Map Estimation (LIME). Performance was evaluated using Structural Similarity Index Measure (SSIM), entropy, Lightness Order Error (LOE), and processing time. SEAHF consistently achieved the lowest LOE across the three datasets, with an overall LOE of 532570.5, while obtaining competitive SSIM (0.5714), entropy (6.0646), and processing time (0.009374 s). Although CLAHE and CHF achieved higher SSIM and CLAHE required less processing time, SEAHF provided the strongest illumination-order preservation while maintaining computational cost comparable to CHF. The results provide initial empirical evidence that spectral-energy-based adaptive cutoff estimation can improve illumination-order preservation while maintaining competitive structural similarity and computational cost.]]>
