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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Bulletin of Electrical Engineering and Informatics
Avin Maulana
Brawijaya University
Electrical & Electronics Engineering Mathematics

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Complex-Valued Neural Networks with Adaptive Frequency Attention for Image Denoising Marjono Marjono; Avin Maulana; Anggi Gustiningsih Hapsani
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.36851

Abstract

Image denoising encompasses various noise types; in this work, we focus specifically on periodic interference, which introduces coherent frequency-domain artifacts that are challenging to remove using conventional real-valued convolutional neural networks (CNNs). This paper introduces a Complex-Valued Neural Network with Adaptive Frequency Attention (CVNN-AFA) tailored to periodic noise removal, integrating complex-domain feature propagation with explicit radial frequency-band modulation. The proposed architecture employs complex convolutions, complex batch normalization, and ModReLU activations to jointly model amplitude and phase information. An Adaptive Frequency Attention (AFA) module operates in the Fourier domain and partitions the spectrum into low-, mid-, and high-frequency radial bands using distance-based masks, enabling adaptive band-wise reweighting aligned with interference characteristics. Experiments on the BSDS500 dataset augmented with synthetic periodic noise evaluate both low-noise and moderate-to-high noise regimes under matched training budgets and strong real-valued frequency-aware baselines. Results indicate that CVNN-AFA achieves competitive performance overall and provides consistent, moderate improvements in low-amplitude settings, while the real-valued frequency-aware baseline remains more robust under extreme corruption levels. Qualitative and spectral analyses suggest that the proposed approach offers incremental attenuation of periodic components while maintaining comparable detail preservation. These findings are specific to the controlled periodic noise scenarios evaluated in this study.
Fractional Perona–Malik-based processing for noise reduction and structure preservation in red, green, blue Pap smear images Syaiful Anam; Normi Abdul Hadi; Avin Maulana; Indah Yanti; Suhaila Abd Halime
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11246

Abstract

Cervical cancer screening relies heavily on Pap smear analysis, yet image noise, low contrast, and overlapping cellular structures continue to limit diagnostic accuracy and the performance of automated systems. This study introduces a fractional Perona–Malik diffusion (FPMD) framework that extends the classical anisotropic diffusion model using fractional-order operators to achieve more flexible, edge-sensitive smoothing. The method is applied to red, green, blue (RGB) Pap smear images and benchmarked against classical PMD and conventional filters using entropy, blind/referenceless image spatial quality evaluator (BRISQUE), and edge preservation index (EPI). FPMD yields substantial improvements, achieving the lowest BRISQUE score (18.88) and the highest EPI values (>0.92) across all channels, indicating superior structural preservation and perceptual quality. While classical PMD produces slightly higher entropy, it introduces artifacts that degrade visual realism. FPMD provides a more controlled enhancement, producing diagnostically meaningful contrast and clearer cytological boundaries. These results highlight its potential as a robust preprocessing tool for both manual assessment and artificial intelligence (AI)-assisted cervical cancer screening.
Co-Authors Anam, Syaiful Anggi Gustiningsih Hapsani Indah Yanti Marjono Marjono Normi Abdul Hadi Suhaila Abd Halime