Peningkatan Kualitas Citra Reduksi Noise Menggunakan Iterative Denoising and Backward Projection-CNN dan TFM-CLAHE Pada Citra 24 Bit
DOI:
https://doi.org/10.33633/tc.v20i4.5243Keywords:
kontras rendah, noise salt, TFM-CLAHE, IDBP-CNN, kualitas citraAbstract
Penurunan kualitas yang diakibatkan adanya noise atau kontras yang tidak normal pada citra mengakibatkan objek pada citra menjadi tidak jelas. Masalah itu dapat disebabkan perangkat yang digunakan menimbulkan noise atau tidak bisa menghasilkan kontras yang normal. Adanya noise dan kontras rendah gelap berdampak besar terhadap kualitas citra?, proses reduksi noise yang berukuran besar 45% akan berpengaruh pada informasi didalam citra sehingga kualitas citra hasil reduksi menjadi hal yang perlu dipertimbangkan untuk noise berukuran besar?. Penelitian tahun 2019 menggunakan algoritma Iterative Denoising and Backward Projections with CNN (IDBP-CNN) dinyatakan mampu mereduksi noise hingga 51% dengan kualitas PSNR diatas 30 dB dengan mengabaikan kontras dari citra. Sedangkan algoritma untuk meningkatkan kontras citra menggunakan algoritma Triangular Fuzzy Membership?Contrast Limited Adaptive Histogram Equalization (TFM-CLAHE) juga diklaim mampu meningkatkan kontras citra dengan kualitas PSNR di atas 20 dB, yang lebih baik dibandingkan dengan algoritma CLAHE. Berdasarkan hasil pengujian yang dilakukan pada 10 citra kontras rendah gelap dengan noise 45% didapatkan kombinasi algoritma TFM-CLAHE diikuti IDBP-CNN lebih baik dengan rata-rata hasil PSNR = 31.69 dB, dibandingkan sebaliknya PSNR = 31.01 dB, Namun rata-rata keragaman informasi citra hasil dengan kombinasi IDBP-CNN diikuti TFM-CLAHE lebih kecil selisihnya terhadap citra asli berdasarkan Shanon Entropy sebesar 3.77% dibandingkan sebaliknya 4.75%References
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