Mass spectrometry imaging (MSI) often suffers from inherent noise due to signal distribution across numerous pixels and low ion counts, leading to shot noise. This can compromise the accurate ...

A self-supervised deep learning model has been developed to improve the quality of dynamic fluorescence images by leveraging temporal gradients. The method enables accurate denoising without ...

Existing methods have limitations in decoupling sharp step edges and flat regions from noisy signals while ensuring the accuracy of step amplitude reconstruction. In this study, we have developed a ...

After the data preprocessing is completed, the next step is to input the processed data into the stacked sparse autoencoder model. The stacked sparse autoencoder is a powerful deep learning ...

Sparse autoencoders (SAEs) are an unsupervised learning technique designed to decompose a neural network’s latent representations into sparse, seemingly interpretable features. While these models have ...

Discover the power of sparse autoencoders in machine learning. Our in-depth article explores how these neural networks compress and reconstruct data, extract meaningful features, and enhance the ...

Keywords: electrocardiogram, sparse coding, multi-measurement vector, artificial intelligence, denoising, deep learning, ADMM Citation: Fotiadou E, Melaet R and Vullings R (2022) Deep unfolding for ...