Hybrid Image Denoising via Sliced Ridgelet Transform and Attention-Guided Deep Learning for Medical and Industrial Applications

Brahmanandam Sharath Gattagoni; Dr Krishnanaik Vankdoth

doi:10.31127/tuje.1928056

Hybrid Image Denoising via Sliced Ridgelet Transform and Attention-Guided Deep Learning for Medical and Industrial Applications

Abstract

Image denoising is a critical task in medical diagnostics and industrial inspection, where noise can significantly degrade structural clarity and compromise analytical accuracy. Unlike conventional approaches that operate directly on noisy inputs, the proposed framework introduces transform-domain preconditioning to simplify the learning space and enhance structural representation prior to learning. The transform-domain stage employs the Sliced Ridgelet Transform to generate sparse, directionally structured representations using Radon projections and adaptive thresholding, preserving edges and structural information. The enhanced representations are refined using an attention-guided residual U-Net with multi-scale feature fusion and perceptual learning. An optional adversarial learning module with a PatchGAN discriminator is included to further improve visual realism. By conditioning the network on transform-domain outputs, the learning process focuses on residual noise suppression and fine texture reconstruction rather than coarse denoising. The proposed framework is evaluated on benchmark datasets, including BraTS 2021, NIH Chest X-ray, and industrial radiographic images, under multiple noise conditions such as Gaussian, speckle, and mixed noise. Experimental results demonstrate consistent and significant improvements over state-of-the-art methods, including DnCNN and SwinIR, achieving higher peak signal-to-noise ratio and structural similarity values along with reduced perceptual error metrics such as LPIPS and FID. The results indicate that the integration of transform-domain sparsity with deep learning-based refinement provides a robust and generalizable solution for complex noise removal, structural preservation, and high-fidelity texture restoration across diverse imaging domains. The proposed framework provides an effective balance between noise suppression, structural preservation, and perceptual quality, making it suitable for real-world medical and industrial imaging applications.

Keywords

Supporting Institution

Chaitanya deemed to be university

Project Number

1928056

Ethical Statement

The authors declare that this research has been conducted in accordance with ethical standards and guidelines. All data used in the study were obtained through ethical research practices, and appropriate permissions were acquired where necessary. No experiments were conducted on humans or animals. All authors have contributed significantly to the research, and there are no conflicts of interest to disclose. The research complies with all relevant laws and regulations and has been carried out with full respect for the principles of integrity, transparency, and accountability.

Thanks

Thank you to the editorial team for your support and valuable guidance.

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Details

Primary Language

English

Subjects

Wireless Communication Systems and Technologies (Incl. Microwave and Millimetrewave), Signal Processing, Communications Engineering (Other)

Journal Section

Research Article

Authors

Brahmanandam Sharath Gattagoni
0009-0004-5578-7127
India

Dr Krishnanaik Vankdoth ^*
0009-0008-9425-5069
India

Publication Date

July 6, 2026

Submission Date

April 11, 2026

Acceptance Date

June 3, 2026

Published in Issue

Year 2026 Volume: 10 Number: 3

DOI

https://doi.org/10.31127/tuje.1928056

IZ

https://izlik.org/JA55TE67LD

Cite

RIS / Bibtex

APA

Gattagoni, B. S., & Vankdoth, D. K. (2026). Hybrid Image Denoising via Sliced Ridgelet Transform and Attention-Guided Deep Learning for Medical and Industrial Applications. Turkish Journal of Engineering, 10(3), 1064-1078. https://doi.org/10.31127/tuje.1928056

AMA

1.Gattagoni BS, Vankdoth DK. Hybrid Image Denoising via Sliced Ridgelet Transform and Attention-Guided Deep Learning for Medical and Industrial Applications. TUJE. 2026;10(3):1064-1078. doi:10.31127/tuje.1928056

Chicago

Gattagoni, Brahmanandam Sharath, and Dr Krishnanaik Vankdoth. 2026. “Hybrid Image Denoising via Sliced Ridgelet Transform and Attention-Guided Deep Learning for Medical and Industrial Applications”. Turkish Journal of Engineering 10 (3): 1064-78. https://doi.org/10.31127/tuje.1928056.

EndNote

Gattagoni BS, Vankdoth DK (July 1, 2026) Hybrid Image Denoising via Sliced Ridgelet Transform and Attention-Guided Deep Learning for Medical and Industrial Applications. Turkish Journal of Engineering 10 3 1064–1078.

IEEE

[1]B. S. Gattagoni and D. K. Vankdoth, “Hybrid Image Denoising via Sliced Ridgelet Transform and Attention-Guided Deep Learning for Medical and Industrial Applications”, TUJE, vol. 10, no. 3, pp. 1064–1078, July 2026, doi: 10.31127/tuje.1928056.

ISNAD

Gattagoni, Brahmanandam Sharath - Vankdoth, Dr Krishnanaik. “Hybrid Image Denoising via Sliced Ridgelet Transform and Attention-Guided Deep Learning for Medical and Industrial Applications”. Turkish Journal of Engineering 10/3 (July 1, 2026): 1064-1078. https://doi.org/10.31127/tuje.1928056.

JAMA

1.Gattagoni BS, Vankdoth DK. Hybrid Image Denoising via Sliced Ridgelet Transform and Attention-Guided Deep Learning for Medical and Industrial Applications. TUJE. 2026;10:1064–1078.

MLA

Gattagoni, Brahmanandam Sharath, and Dr Krishnanaik Vankdoth. “Hybrid Image Denoising via Sliced Ridgelet Transform and Attention-Guided Deep Learning for Medical and Industrial Applications”. Turkish Journal of Engineering, vol. 10, no. 3, July 2026, pp. 1064-78, doi:10.31127/tuje.1928056.

Vancouver

1.Brahmanandam Sharath Gattagoni, Dr Krishnanaik Vankdoth. Hybrid Image Denoising via Sliced Ridgelet Transform and Attention-Guided Deep Learning for Medical and Industrial Applications. TUJE. 2026 Jul. 1;10(3):1064-78. doi:10.31127/tuje.1928056