Methodology: Introduction of the diffusion-scheduled denoising autoencoder (DDAE) that integrates diffusion noise scheduling into the denoising autoencoder framework. Representation: Extension of DDAE ...

Hybrid approaches Modern denoising combines low-rank modeling, sparse coding, and transform-based filtering. Techniques like WNNM and deep plug-and-play priors integrate classical and learned ...

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 ...

Removing noise and recovering the micronewton thrust signal are of great significance in high-precision static thrust measurements. Typically, the micronewton thrust signal is in the shape of a ...

Suboptimal outputs and high resource consumption have thus put forth the need for a new method that can efficiently denoise the corrupted data. The proposed method, Discrete Diffusion with Planned ...

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 ...

The previous works only consider shallow networks and low-resolution datasets thus making difficult to compare these results with state of the art non-spiking autoencoders for image denoising (Zhang ...