Evaluating BM3D and NBNet: A Comprehensive Study of Image Denoising Across Multiple Datasets

TL;DR

This comprehensive study compares traditional BM3D and modern NBNet image denoising methods across multiple datasets, evaluating their effectiveness with IQA metrics and object detection performance to understand their strengths and limitations.

Contribution

The paper provides a detailed comparative analysis of BM3D and NBNet across diverse datasets and noise conditions, highlighting their respective advantages and limitations in real-world scenarios.

Findings

BM3D performs well in blur challenges.

NBNet is more effective in complex noise like under/over-exposure.

Different denoising methods excel in different noise environments.

Abstract

This paper investigates image denoising, comparing traditional non-learning-based techniques, represented by Block-Matching 3D (BM3D), with modern learning-based methods, exemplified by NBNet. We assess these approaches across diverse datasets, including CURE-OR, CURE-TSR, SSID+, Set-12, and Chest-Xray, each presenting unique noise challenges. Our analysis employs seven Image Quality Assessment (IQA) metrics and examines the impact on object detection performance. We find that while BM3D excels in scenarios like blur challenges, NBNet is more effective in complex noise environments such as under-exposure and over-exposure. The study reveals the strengths and limitations of each method, providing insights into the effectiveness of different denoising strategies in varied real-world applications.