Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement
Recently, deep learning approaches have become the main research frontier for biological image reconstruction and enhancement problems thanks to their high performance, along with their ultra-fast inference times. However, due to the difficulty of obtaining matched reference data for supervised learning, there has been increasing interest in unsupervised learning approaches that do not need paired reference data. In particular, self-supervised learning and generative models have been successfully used for various biological imaging applications. In this paper, we overview these approaches from a coherent perspective in the context of classical inverse problems, and discuss their applications to biological imaging, including electron, fluorescence and deconvolution microscopy, optical diffraction tomography and functional neuroimaging.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep LearningImage ReconstructionSelf-Supervised LearningSimilar Papers 제목 키워드 기반
Recent Advancements in Microscopy Image Enhancement using Deep Learning: A Survey
Microscopy image enhancement plays a pivotal role in understanding the details of biological cells and materials at microscopic scales. In recent years, there has been a significant rise in the advancement of microscopy …
Image EnhancementLLGS: Unsupervised Gaussian Splatting for Image Enhancement and Reconstruction in Pure Dark Environment
3D Gaussian Splatting has shown remarkable capabilities in novel view rendering tasks and exhibits significant potential for multi-view optimization.However, the original 3D Gaussian Splatting lacks color representation …
Image EnhancementLL-GaussianMap: Zero-shot Low-Light Image Enhancement via 2D Gaussian Splatting Guided Gain Maps
Significant progress has been made in low-light image enhancement with respect to visual quality. However, most existing methods primarily operate in the pixel domain or rely on implicit feature representations. As a res…
Low-Light Image EnhancementBBOmix: A Tabular Benchmark for Hyperparameter Optimization of Unsupervised Biological Representation Learning
The rapid advancement of high-throughput sequencing has led to large, high-dimensional omics datasets. Deep unsupervised learning architectures, particularly Autoencoders (AEs), are increasingly used for dimensionality r…
Hyperparameter OptimizationDimensionality ReductionRepresentation LearningTransfer LearningDiffuseIR:Diffusion Models For Isotropic Reconstruction of 3D Microscopic Images
Three-dimensional microscopy is often limited by anisotropic spatial resolution, resulting in lower axial resolution than lateral resolution. Current State-of-The-Art (SoTA) isotropic reconstruction methods utilizing dee…
Super-Resolution