Core Concepts
A fast iterative algorithm is proposed for noise reduction in images and optical coherence tomography, without requiring training or ground truth data, and can handle independent noise as well as correlated (coherent) noise.
Abstract
The paper introduces a fast iterative algorithm called "Back to Basics" (BTB) for noise reduction in images and optical coherence tomography (OCT). The key highlights are:
The algorithm is computationally efficient and does not require training or ground truth data. It can handle both independent noise (e.g., additive white Gaussian noise) and correlated (coherent) noise (e.g., Poisson noise, speckle noise in OCT).
For additive white Gaussian noise, the algorithm demonstrates performance comparable to state-of-the-art methods like BM3D and TNRD, while requiring very few iterations to converge.
For Poisson noise, the algorithm effectively suppresses noise while preserving structural details, outperforming TNRD and RED-SD.
For speckle noise in OCT, the algorithm employs a Receptive Field Normalization (RFN) operator to efficiently suppress speckle patterns, without requiring any training data or knowledge of the noise level.
Theoretical guarantees are provided for the convergence stability of the proposed iterative algorithm under certain conditions.
The paper demonstrates the versatility and effectiveness of the BTB algorithm across different noise models, including natural image denoising, Poisson noise reduction, and speckle suppression in OCT.
Stats
The paper presents several quantitative metrics to evaluate the performance of the proposed BTB algorithm:
For natural image denoising with additive white Gaussian noise, the PSNR scores are reported for the Set10 and BSD68 datasets, and compared to BM3D and TNRD.
For Poisson noise reduction, the PSNR scores are reported for the Set10 and BSD68 datasets, and compared to TNRD and RED-SD.
For speckle suppression in OCT, a synthetic example is used to visualize the evolution of the speckle degree over the iterations of the algorithm.