Towards scanning electron microscopy image denoising: a state-of-the-art overview, benchmark, taxonomies, and future direction

Scanning electron microscope (SEM) enables imaging of micro-nano scale objects. It is an analytical tool widely used in the material, earth and life sciences. However, SEM images often suffer from high noise levels, influenced by factors such as dwell time, the time during which the electron beam remains per pixel during acquisition. Slower dwell times reduce noise but risk damaging the sample, while faster ones introduce uncertainty. To this end, the latest state-of-the-art denoising techniques must be explored. Experimentation is crucial to identify the most effective methods that balance noise reduction and sample preservation, ensuring high-quality SEM images with enhanced clarity and accuracy. A thorough analysis tracing the evolution of image denoising techniques was conducted, ranging from classical methods to deep learning approaches. A comprehensive taxonomy of this reverse problem solutions was established, detailing the developmental flow of these methods. Subsequently, the latest state-of-the-art techniques were identified and reviewed based on their reproducibility and the public availability of their source code. The selected techniques were then tested and investigated using scanning electron microscope images. After in-depth analysis and benchmarking, it is clear that the existing deep learning-based denoising techniques fall short in maintaining a balance between noise reduction and preserving crucial information for SEM images. Issues like information removal and over-smoothing have been identified. To address these constraints, there is a critical need for the development of SEM image denoising techniques that prioritize both noise reduction and information preservation. Additionally, one can see that the combination of several networks, such as the generative adversarial network and the convolutional neural network (CNN), known as BoostNet, or the vision transformer and the CNN, known as SCUNet, improves denoising performance. It is recommended to use blind techniques to denoise real noise while taking into account detail preservation and tackling excessive smoothing, particularly in the context of SEM. In the future the use of explainable AI will facilitate the debugging and the identification of these problems.