Implicit Neural Image Field for Biological Microscopy Image Compression
The rapid pace of innovation in biological microscopy imaging has led to large images, putting pressure on data storage and impeding efficient sharing, management, and visualization. This necessitates the development of efficient compression solutions. Traditional CODEC methods struggle to adapt to the diverse bioimaging data and often suffer from sub-optimal compression. In this study, we propose an adaptive compression workflow based on Implicit Neural Representation (INR). This approach permits application-specific compression objectives, capable of compressing images of any shape and arbitrary pixel-wise decompression. We demonstrated on a wide range of microscopy images from real applications that our workflow not only achieved high, controllable compression ratios (e.g., 512x) but also preserved detailed information critical for downstream analysis.
Code (1)
Tasks
Image CompressionManagementSimilar Papers 제목 키워드 기반
Diffusion-Based Synthetic Brightfield Microscopy Images for Enhanced Single Cell Detection
Accurate single cell detection in brightfield microscopy is crucial for biological research, yet data scarcity and annotation bottlenecks limit the progress of deep learning methods. We investigate the use of uncondition…
Synthetic Data GenerationObject DetectionCell DetectionUnsupervised Representations of Pollen in Bright-Field Microscopy
We present the first unsupervised deep learning method for pollen analysis using bright-field microscopy. Using a modest dataset of 650 images of pollen grains collected from honey, we achieve family level identification…
ClusteringDeep Learning Enables Large Depth-of-Field Images for Sub-Diffraction-Limit Scanning Superlens Microscopy
Scanning electron microscopy (SEM) is indispensable in diverse applications ranging from microelectronics to food processing because it provides large depth-of-field images with a resolution beyond the optical diffractio…
Defect DetectionImage-to-Image TranslationSuper-ResolutionViTally Consistent: Scaling Biological Representation Learning for Cell Microscopy
Large-scale cell microscopy screens are used in drug discovery and molecular biology research to study the effects of millions of chemical and genetic perturbations on cells. To use these images in downstream analysis, w…
Drug DiscoveryRepresentation LearningOptimising image capture for low-light widefield quantitative fluorescence microscopy
Low-light optical imaging refers to the use of cameras to capture images with minimal photon flux. This area has broad application to diverse fields, including optical microscopy for biological studies. In such studies, …
Denoising