Synchrotron experiments generate large volumes of data with diverse sources of noise, making image denoising a crucial task for obtaining high-quality images. Traditional denoising techniques have been developed and applied in this context, but modern machine learning-based methods have shown great promise in achieving higher denoising performance.
In this talk, we review the challenges of denoising in the light of the diversity and size of synchrotron data at scale and evaluate the effectiveness of traditional denoising techniques and modern machine learning-based methods in handling these challenges. We also emphasize the importance of user-friendliness and ease of appropriation of the denoising tools, particularly for machine learning-based methods.
We break down the talk into three parts: image denoising in the context for spectral inverse space, image denoising in the real space like tomography, and attempts at inverse problems (missing angle, compressed images). For each of these areas, we provide an overview of common sources of noise, review traditional denoising techniques and modern machine learning-based methods, evaluate their effectiveness in handling diverse synchrotron data at Soleil, and discuss their user-friendliness and ease of adoption.
The talk aims to provide insights into the importance of balancing denoising effectiveness and ease of use, particularly for modern machine learning-based denoising techniques, in synchrotron data. We conclude with suggestions for further research and development of user-friendly and accessible denoising techniques, with a focus on modern machine learning-based methods, for synchrotron data.
