Image and Signal Denoising Methods

Enhancing Biomedical Optical Volumetric Imaging via Self-Supervised Orthogonal Learning

This paper introduces VALID, a self-supervised denoising method for volumetric biomedical imaging that improves structural fidelity across multiple modalities.

Comparison of Deep Learning Approaches for Extreme Low-SNR Image Restoration

The paper presents a dataset for evaluating deep learning denoising methods on low-SNR fluorescence microscopy images and compares three models, finding the supervised transformer-based model performs best but requires more training time.

HD2Net: A Deep Learning Framework for Simultaneous Denoising and Deaberration in Fluorescence Microscopy

HD2Net improves fluorescence microscopy image quality by simultaneously denoising and correcting aberrations without additional hardware, outperforming existing methods in resolution and contrast.

Denoising complex-valued diffusion MR images using a two-step non-local principal component analysis approach

This paper introduces a two-step non-local PCA method for denoising diffusion MRI with few diffusion directions, improving image quality and DTI metric estimation.

Robust self-supervised denoising of voltage imaging data using CellMincer

CellMincer is a self-supervised deep learning method for denoising voltage imaging data, achieving significant noise reduction and improved functional phenotype separation.

DeepVID v2: Self-Supervised Denoising with Decoupled Spatiotemporal Enhancement for Low-Photon Voltage Imaging

DeepVID v2 improves low-photon voltage imaging by resolving spatial and temporal noise through self-supervised denoising with decoupled enhancement.

FONDUE: Robust resolution-invariant denoising of MR Images using Nested UNets

This paper introduces FONDUE, a deep learning method for denoising MRI images that works across varying resolutions and performs comparably to state-of-the-art methods.

Baikal: Unpaired Denoising of Fluorescence Microscopy Images using Diffusion Models

Baikal improves fluorescence microscopy image denoising using diffusion models without requiring paired noisy and clean images.

Denoising Diffusion MRI: Considerations and implications for analysis

This paper evaluates dMRI denoising methods by defining performance criteria and comparing patch-based algorithms in complex and magnitude domains.

IMC-Denoise: a content aware denoising pipeline to enhance Imaging Mass Cytometry

IMC-Denoise improves Imaging Mass Cytometry image quality by reducing noise and artifacts, enhancing downstream analysis.

Denoising diffusion weighted imaging data using convolutional neural networks

This paper introduces a 1D-CNN denoising method for DWI data that improves image quality and microstructural measurements by learning from low-noise datasets.

Deep-learning microscopy image reconstruction with quality control reveals second-scale rearrangements in RNA polymerase II clusters

The paper presents a deep-learning workflow for fluorescence microscopy image reconstruction with quality control, revealing dynamic changes in RNA polymerase II clusters in zebrafish embryos.

Real-time Image Denoising of Mixed Poisson-Gaussian Noise in Fluorescence Microscopy Images using ImageJ

The paper develops a CNN-based denoising method for mixed Poisson-Gaussian noise in fluorescence microscopy images, achieving real-time performance and superior SNR improvement.

Noise-Transfer2Clean: Denoising cryo-EM images based on noise modeling and transfer

Noise-Transfer2Clean improves cryo-EM image denoising by modeling and transferring noise characteristics, achieving better signal restoration than existing methods.

Time-Dependent Image Restoration of Low-SNR Live Cell Ca2+ Fluorescence Microscopy Data

This paper introduces a variational image restoration method for low-SNR live cell Ca2+ fluorescence microscopy data, demonstrating its effectiveness across synthetic and biological datasets.

Removing independent noise in systems neuroscience data using DeepInterpolation

DeepInterpolation improves signal-to-noise ratio in neuroscience data without sacrificing resolution, enhancing detection of neuronal activity across multiple modalities.

Reinforcing neuron extraction and spike inference in calcium imaging using deep self-supervised learning

This paper introduces DeepCAD, a self-supervised learning method to enhance calcium imaging by reducing noise and improving SNR, leading to better neuron extraction and spike inference.

Denoising High-field Multi-dimensional MRI with Local Complex PCA

This paper introduces a denoising technique for multi-parametric quantitative MRI that improves signal-to-noise ratio without compromising spatial accuracy or creating spurious boundaries.

A Fully Automated, Faster Noise Reduction Approach to Increasing the Analytical Capability of Chemical Imaging for Digital Histopathology

This paper introduces automated noise reduction methods for hyperspectral imaging data, achieving faster processing with comparable accuracy to manual methods.