Deep Learning Image Enhancement for Coherent Laser Speckle Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Imaging devices using incoherent light sources face issues with excessive size, heat generation, cost, and poor lifespan, while coherent light sources suffer from laser speckle noise, which conventional methods to mitigate, such as bandwidth widening or mechanical vibration, compromise the advantages of coherent energy.
Innovation Solution
Employing a deep learning technique with generative adversarial networks (GANs) to process coherent energy illuminated images, reducing laser speckle noise and enhancing image quality, enabling high-speed imaging and low-light conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If incoherent light sources are used for illumination, then image quality is improved, but device size, heat generation, cost, and lifespan are adversely affected
Solution Approach 1:
The patent replaces the physical optical system (incoherent light source, fiber bundle, illumination optics) with a computational system. A camera captures raw images under coherent illumination, and a neural network processes these images to generate enhanced output images, substituting hardware complexity with software-based image enhancement.
Solution Approach 2:
The patent extracts the harmful laser speckle noise from the captured image using neural network processing. The neural network identifies and removes speckle patterns while preserving the underlying anatomical structures, separating the desired image information from the unwanted noise.
2Device complexity
If coherent light sources are used for illumination, then device size and cost are reduced, but laser speckle noise degrades image quality
Solution Approach 1:
The patent converts the harmful laser speckle noise into a beneficial training signal for the neural network. The network is trained on pairs of images (one with speckle, one without) to learn the transformation, effectively using the speckle noise as part of the learning process to remove it in new images.
Solution Approach 2:
Instead of using mechanical methods to reduce speckle (such as moving parts or optical diffusers), the patent substitutes a computational approach where a neural network processes the speckled images to generate speckle-free output, replacing mechanical complexity with intelligent algorithms.
3Measurement precision
If conventional methods are used to reduce laser speckle, then image quality is improved, but the advantages of coherent energy illumination are compromised
Solution Approach 1:
The patent replaces mechanical speckle reduction methods (bandwidth widening, mechanical vibration, optical diffusers) with a computational neural network approach. This allows coherent illumination to be used without the mechanical complications, preserving the advantages of coherent light while achieving speckle reduction through software.
Solution Approach 2:
The patent changes the parameter space from optical domain (wavelength, bandwidth, optical path) to computational domain (pixel values, feature representations). By processing images in the computational domain rather than modifying optical parameters, the system preserves coherent illumination benefits while removing speckle through algorithmic transformation.
Data Source
AI summary
A device may receive a coherent energy illuminated image, of a particular object, that includes laser speckle. The device may process, using a laser speckle reduction model, the coherent energy illuminated image to generate a laser speckle-reduced image. The device may provide the laser speckle-reduced image as output to permit diagnostics based on the laser speckle-reduced image.