ArticleSensors (Basel, Switzerland)2024
Enhanced Wavelet-Based Medical Image Denoising with Bayesian-Optimized Bilateral Filtering.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Real-time wavelet threshold denoising for laser speckle blood flow imaging.Scientific reports · 2026Article
- VMamba-QAG-net: a five-stage pipeline for SYNTAX score computation and decision support in interventional cardiology using X-ray angiography.Frontiers in artificial intelligence · 2026Article
- Optimized Lung Nodule Classification Using CLAHE-Enhanced CT Imaging and Swin Transformer-Based Deep Feature Extraction.Journal of imaging · 2025Article
- Recurrent Flow Update Model Using Image Pyramid Structure for 4K Video Frame Interpolation.Sensors (Basel, Switzerland) · 2025Article
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Abstract
Medical image denoising is essential for improving the clarity and accuracy of diagnostic images. In this paper, we present an enhanced wavelet-based method for medical image denoising, aiming to effectively remove noise while preserving critical image details. After applying wavelet denoising, a bilateral filter is utilized as a post-processing step to further enhance image quality by reducing noise while maintaining edge sharpness. The bilateral filter's effectiveness heavily depends on its parameters, which must be carefully optimized. To achieve this, we employ Bayesian optimization, a powerful technique that efficiently identifies the optimal filter parameters, ensuring the best balance between noise reduction and detail preservation. The experimental results demonstrate a significant improvement in image denoising performance, validating the effectiveness of our approach.
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