Lensless Imaging Depth of Field Extension via Computational Reconstruction
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Solution Overview
Problem
Lensless imaging with holographic reconstruction algorithms struggles to produce clear images of samples containing particles at varying distances from the image sensor, resulting in hazy images for some particles due to limited depth of field.
Innovation Solution
A method involving the acquisition of a stack of complex images at different reconstruction distances, computation of clearness indicators, and selection of optimal images for each radial position to form a clear observation image, allowing particles at various distances to appear clear without requiring multiple image acquisitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single image acquisition is performed in lensless imaging, then the acquisition time is short and the process is simple, but particles at various distances from the image sensor appear hazy due to limited depth of field
Solution Approach 1:
The patent segments the imaging process by dividing the sample space into multiple depth layers along the propagation axis. Instead of attempting to capture all particles at once, the system performs sequential image acquisitions at different reconstruction distances, processing each depth layer independently and then combining the results to achieve full-depth clarity.
Solution Approach 2:
The patent applies preliminary action by performing multiple image acquisitions at different reconstruction distances before final image synthesis. The system pre-processes images at various depths, identifies clear particles in each layer, and prepares them for combination, ensuring that all particles regardless of depth are properly focused before the final observation image is constructed.
2Measurement precision
If multiple image acquisitions are performed at different reconstruction distances, then particles at various distances appear clear, but the acquisition time increases and the process becomes more complex
Solution Approach 1:
The patent merges multiple images acquired at different reconstruction distances into a single composite observation image. By combining the clear particles from each depth layer according to their spatial positions, the system achieves comprehensive clarity across all depths while managing the complexity through automated selection and synthesis algorithms.
Solution Approach 2:
The patent applies local quality by allowing different regions of the final image to be derived from different source images based on their depth positions. Each particle in the observation image is selected from the reconstruction distance where it appears clearest, creating a composite image with locally optimized quality for each spatial position.
3Measurement precision
If the depth of field is increased to capture particles at various distances clearly, then image clarity is improved, but the device complexity increases due to multiple image acquisitions and processing steps
Solution Approach 1:
The patent replaces mechanical focusing mechanisms with numerical propagation operators and holographic reconstruction algorithms. Instead of physically adjusting focus for different depths, the system uses computational methods to reconstruct images at various reconstruction distances, significantly reducing mechanical complexity while achieving extended depth of field.
Solution Approach 2:
The patent changes the reconstruction distance parameter across multiple acquisitions to achieve focus at different depths. By systematically varying this parameter and combining the results, the system extends the effective depth of field without requiring complex optical mechanisms, relying instead on parameter variation and computational synthesis.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach increases the depth of field in lensless imaging, enabling clear imaging of particles at various distances from the image sensor in a single acquisition, improving the characterization and representation of samples.
Implementation Method 1
a light source configured to emit an incident light wave that propagates toward the sample
Implementation Method 2
an image sensor configured to form an image of the sample in a detection plane
Implementation Method 3
forming a stack of complex images, called reconstructed images, from the image acquired in step b), each reconstructed image being obtained by applying, for one reconstruction distance along the propagation axis, a numerical propagation operator
Implementation Method 4
The application of a holographic reconstruction algorithm allows profiles representative of the light wave to which the image sensor is exposed to be formed
Data Source
AI summary
Method for observing a sample comprising the steps of (a) illuminating the sample using a light source, the light source emitting an incident light wave that propagates toward the sample along a propagation axis (Z); (b) acquiring, using an image sensor, an image of the sample, which image is formed in a detection plane; (c) forming a stack of images, called reconstructed images, from the image acquired in step (b), each reconstructed image being obtained by applying, for one reconstruction distance, a numerical propagation operator; and (d) from each image of the stack of images, computing a clearness indicator for various radial positions, each clearness indicator being associated with one radial position and with one reconstruction distance.