LFRanker: An Iterative Method for Identifying Top-Performing Loss Functions in SEM Image Denoising

This work focuses on identifying the best performing loss functions for denoising scanning electron microscopy (SEM) images. Recent studies on the impact and effect of loss functions in image denoising are multiplying, particularly in the fields of low-light and microscopic imaging. Most studies have focused on hybrid loss functions, which are a mixture of multiple loss functions, and have shown their significance. However, manual experiments with different loss functions to build a hybrid function are generally very time-consuming. Thus, we propose a framework named LFRanker (Loss Function Ranker) that automatically and iteratively experiments loss function from a given set. LFRanker automatically calculates combinations using set theory from a given set of loss functions. The findings indicated significant variations across the different assessment criteria (such as PSNR, SSIM and keypoints preservation). In this work, we focus on SEM image denoising, which may be subject to reconstruction in the next step. Thus, it is necessary to preserve the maximum keypoints for point-cloud reconstruction during denoising, along with high quality. Based on these criteria, we found a hybrid loss function, combining L<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MSE</inf>, L<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MAE</inf>, L<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PER</inf> and L<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SIM</inf> performs best. Finally, LFRanker demonstrates the ability to automate manual experimental efforts in identifying appropriate loss functions for denoising.