Improved Contrast and Clarity in Plant Microscopic Images using Contrast Limited Adaptive Histogram Equalization - Eka Wahyu Hidayat; R Reza El Akbar; Muhammad Adi Khairul Anshary (Informatika, Universitas Jenderal Soedirman, 2026)
This research aims to enhance the quality of microscopic plant images which often suffer from low contrast and noise, hindering both visual and automated analysis. We propose the application of the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm to address this issue. Implementation was carried out using MATLAB, processing a dataset of microscopic images from the Biology Laboratory of Siliwangi University. The research methodology includes image pre-processing, applying CLAHE with a Tile Grid Size of 8×8 and a Clip Limit of 0.02, and a quantitative evaluation using full-reference metrics such as MSE, PSNR, SSIM, RMSE, and FSIM. The results show that the application of CLAHE consistently demonstrated a significant improvement in image quality. Based on calculations, the lowest MSE value was found in the “monokotil (L.S)” image with 644.046 and the highest in the Monocotyledon Stem image with 6,298,683. The highest PSNR value was achieved by the “monokotil (L.S)” image with 46.225 dB, while the lowest was in two Monocotyledon Stem images, at 25.174 dB and 23.422 dB. The highest SSIM value was also in the “monokotil (L.S)” image with 0.946, indicating a very high structural similarity. Likewise, the highest FSIM value was also found in the “monokotil (L.S)” image with 0.979. This enhancement is crucial for botanical analysis and bioinformatics applications, as it effectively increases contrast, reduces noise, and preserves structural integrity, thereby facilitating the identification of fine details in microscopic images. These results establish a reproducible enhancement baseline that strengthens downstream botanical analytics.