Hybrid Image Denoising Framework Based on Transform-Domain Signal Enhancement Using Sliced Ridgelet Transform for Medical and Industrial Applications

Abstract

Effective image denoising is crucial for ensuring high-fidelity results in medical diagnostics and industrial defect analysis. This paper presents the first phase of a two-phase AI-augmented denoising framework using a Sliced Ridgelet Transform (SRT) - based preprocessing module. Phase I aims to convert noisy images into sparse, edge-preserving representations through advanced transform techniques including Radon projections, ridgelet decomposition, and adaptive thresholding. Results on benchmark datasets demonstrate significant noise suppression, enhanced directional sensitivity, and improved coefficient sparsity, forming a robust foundation for AI-based refinement in Phase II.

Keywords

• Sliced ridgelet transform
• Radon projection
• Image denoising
• Bit-plane slicing
• Ridgelet coefficients
• Medical imaging
• Transform-domain processing

Supporting Institution

Chaitanya Deemed to be University, Hyderabad, Telangana, India

Project Number

123456

Ethical Statement

The authors affirm that this research was conducted in accordance with recognized ethical standards. All data used in this study were obtained from publicly available datasets, open-source repositories, or simulated sources, and no human participants or animals were directly involved in the experimental procedures. The work does not contain any studies requiring ethical approval from an institutional review board or ethics committee. The authors declare that the research was carried out with academic integrity, without any form of plagiarism, data fabrication, falsification, or inappropriate manipulation. Proper citation and acknowledgment have been provided for all prior works and references. The study complies with accepted ethical practices in research, authorship, and publication, ensuring transparency, accuracy, and fairness in reporting results.

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