5 results about "Focus stacking" patented technology

This invention provides an adaptive image focus stacking method, system, and pathological slide scanner. It acquires multiple frames of original images from different focal planes within the same field of view, performs perceptual preprocessing on each frame, conducts multi-scale spatial frequency evaluation on each preprocessed frame, performs adaptive raster registration based on the multi-scale weight map, performs intelligent decision fusion on the corrected frames, and performs spatial consistency verification on the fused image, outputting a final full-area sharp focus stacked image. This invention offers advantages such as adaptive processing of high dynamic range images, improved robustness of sharpness evaluation, optimized alignment efficiency, and improved fusion effect, thereby generating high-quality full-area sharp focus stacked images.

Systems, devices and methods for sorting particles utilizing focus stacking of two-dimensional images are described. Such systems, devices and methods may further provide for particle processing and may encompass, on a microfluidic scale, sample enrichment, sample mixing, sample / particle sorting, verification of sorting and feedback-based optical sorting.

Implementations described herein relate to modifying depth of field in images using machine learning. In some implementations, a computer-implemented method for training a machine learning model includes generating an input training image that is a composition of multiple images captured in focus stacks at different lens focus positions and camera distances. A corresponding ground truth image is generated from merged images in particular focus stacks. A convolutional neural network (CNN) machine learning (ML) model receives the input training image and outputs an output image that adjusts blurriness in the input training image to simulate a target depth of field. The CNN ML model is updated based on comparison of the output image and the ground truth image. The CNN ML model can include a depth CNN that performs an implicit depth estimation for features of the input image, and a deconvolution CNN that adjusts the blurriness.

The invention provides a flame three-dimensional multi-physics field reconstruction method based on a light field focus stack, which comprises the following steps of: imaging blackbody radiation sources at different temperatures by using a light field camera, and establishing and outputting a fitting relationship between response values of color channels of a light field refocusing image and corresponding spectral radiation intensities according to a Planck law; acquiring a flame light field focus stack image sequence of the flame light field original image by adopting a super-resolution reconstruction method based on a sub-aperture image; and outputting temperature distribution and soot concentration distribution of the corresponding flame slice image by using the three-dimensional convolutional neural network model constrained by the fused radiation transmission physical information. According to the method, the implicit correlation model between the flame focus slice and the three-dimensional physical field of the flame focus slice is directly constructed by fusing the three-dimensional convolutional neural network model constrained by the forward radiation transmission physical information, and the generalization ability and the interpretability of the model can be improved, so that the joint reconstruction work of the physical field is more effectively realized.

The application discloses a focus depth estimation method and device based on an event focus stack, which comprises the following steps: taking a full-focus image and a corresponding scene depth map of a target scene by using a camera, calculating a defocus blur quantity, and post-rendering the full-focus image to obtain a focus stack, and simulating the focus stack to obtain an event focus stack; preprocessing the event focus stack by using a voxel grid and a depth surface coding method to obtain an event tensor, and constructing a multi-scale neural network to evaluate the focus degree of the event focus stack and estimate the scene depth, wherein the multi-scale neural network comprises a focus information extraction module, a depth regression module and a depth enhancement module; using the estimated scene depth to train the multi-scale neural network in a supervised framework to obtain a target multi-scale neural network; and inputting the event tensor into the target network model to reconstruct a scene depth image to estimate the target scene depth.