A Lightweight Training-Free Image Enhancement Method for Traditional Sikka Ikat Motifs Using Log-Gabor Enhanced CLAHE (LGE-CLAHE)
Received: 12 December 2025 | Revised: 6 January 2026 | Accepted: 17 January 2026 | Online: 9 February 2026
Corresponding author: Nur Rokhman
Abstract
Image enhancement is essential to make subtle motifs in traditional Sikka ikat more visible, where uneven illumination and complex woven textures often hinder effective contrast enhancement. To address this challenge, this paper proposes a training-free and computationally lightweight image enhancement method, termed Log-Gabor Enhanced Contrast-Limited Adaptive Histogram Equalization (LGE-CLAHE). The proposed method integrates a texture-preserving Log-Gabor branch and a contrast-regulating CLAHE branch through a histogram-guided, block-wise adaptive weight map derived from local luminance statistics. Unlike fixed-weight hybrid schemes, the proposed adaptive fusion dynamically adjusts each branch's contribution based on spatial texture variation, enabling robust enhancement under non-uniform illumination without requiring training data. The proposed method is evaluated on a dataset of 432 Sikka ikat images representing 24 traditional motifs, captured under morning, daytime, and night lighting conditions. Experimental results demonstrate that LGE-CLAHE consistently outperforms classical enhancement methods and single-branch baselines, achieving a PSNR of 24.31 dB, an SSIM of 0.961, and an MAE of 0.051. Visual evaluations further confirm that the proposed method enhances motif clarity while avoiding common artifacts such as haloing, block boundaries, and excessive contrast amplification. Owing to its training-free design and low computational complexity, LGE-CLAHE is well-suited to cultural heritage documentation and other low-resource image enhancement applications.
Keywords:
LGE-CLAHE, Sikka ikat, Log-Gabor filter, contrast enhancement, texture preservationDownloads
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Copyright (c) 2026 Imelda Dua Reja, Nur Rokhman, Agus Sihabuddin
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