Image Enhancement TRIZ Cases

A blur correction device updates sensor data to refine point spread function estimates for captured images.

Automated LiDAR point cloud processing classifies vegetation data to identify hazardous areas near power lines.

Multi-scale image signal processor generates illuminance maps via iterative FGWLS operations, preventing blocking artifacts in low-illumination environments.

A six degrees of freedom tracking device combines optical cameras with an inertial navigation unit for precise spatial data.

A hybrid deep learning model extracts color and texture features to classify photographic scenes.

A diffusion encoding sequence uses orthogonal magnetic field gradients to encode tissue microstructure data.

A correction apparatus synthesizes weighted basis maps to fix color shading artifacts in captured images.

A point cloud attribute coding method applies a YCoCg-R transform to decorrelate inter-channel dependencies for efficient reconstruction.

Personalizing AI medical imaging analysis through continuous user feedback loops that resolve regional preference mismatches and improve radiologist acceptance.

A motion estimation method segments searching areas into groups to calculate total differences simultaneously.

Machine learning separates reflectance and flow signals to distinguish shadows from perfusion defects, improving vessel density measurement accuracy.

A deep convolutional neural network classifies malignancy likelihood using multi-channel image representations derived from sequential breast MRI scans.

Linear combination of multiple die images creates a computed reference that minimizes the norm of the difference image, improving defect detection sensitivity.

A digital camera applies distinct image processing levels to detected face regions based on their distance from the lens.

Detecting embedded noise patterns differentiates authentic items from high-quality copies, resolving inspection accuracy challenges.

Radio-opaque markers align volumetric CT scans with surface optical data, eliminating scan stents and reducing patient visits.

A control unit dynamically switches between detection programs of varying accuracy to track objects in video data.

Region-based correction equations account for brightness and contrast variations in divided image areas, improving training data expression accuracy.

A display controller extracts a region of interest from captured frames to maintain continuous visibility for remote support operators.

A multi-camera system links narrow and wide field-of-view lenses to move simultaneously along a common axis for component inspection.

A processor co-registers extraluminal roadmap images with endoluminal data points to generate a synchronized display for real-time navigation.

A microscope control device determines relative spatial position of a marking element from optical and tomographic recordings.

Extends VIO position estimates across time periods to maintain consistency, resolving GNSS accuracy loss from urban sky obstructions.

A multi-camera imaging device processes thermal and visible light data to generate composite images with unified fields of view.

Optical modules calibrate content position in head-worn displays, resolving accuracy versus complexity trade-offs through segmentation.

Spatiotemporal singular value decomposition isolates blood vessel signals from ultrasound data, resolving small vessels obscured by tissue scattering.

A diagnosis assistance system segments digital histopathology images into super-pixels and uses shape compactness to define biological object contours.

A system aligns wire models in video frames to generate 3D annotations for machine learning training.

An image processing apparatus selects a specific tracking algorithm to follow an analysis portion in radiographic frames.

Stereo reconstruction pipeline aligns depth maps and identifies outliers to resolve error accumulation in 3D modeling.

A determination device uses sensor data and a learned model to label target portions as good, defect, or candidate.

Automated apparatus classifies collagenous and elastic fibers to calculate occupancy rates for tissue analysis.

Automated feedback schemes normalize projector characteristics to maintain consistent image quality across theaters and over time.

Laser etched grayscale and geometric patterns on vials standardize camera settings and spatial alignment across multiple inspection lines.

Selective RF data transfer from ultrasound probes reduces power dissipation and hardware complexity during ultrafast imaging.

A surveillance system uses object recognition to identify sensitive data and applies pixel scrambling or blur-kernels for obfuscation.

A wearable apparatus projects light patterns to indicate the active image sensor field of view for improved user interaction.

A vehicle imaging device rotates captured images to align front and rear regions with actual vehicle directions.

Joint image reduction uses a uniform ratio for multiple models, cutting processing time by 40% while maintaining accuracy.

A system aligns query photographs with reference images to determine assembly pose and map two-dimensional pixels to three-dimensional engineering data.

Patsnap Eureka analyzes a patent where a deep learning model detects histology slide artefacts early, preventing wasted pathologist time on unusable samples.

Fourier transform analysis of spatial frequency replaces complex histological methods, ensuring reliable transplantation suitability.

A single camera system estimates object dimensions by normalizing images through a pre-trained deep learning model.

A computer-implemented method enhances vehicle positioning by analyzing onboard images to identify known visual objects and calculate relative coordinates.

A system estimates available storage volumes using imaging data from digital cameras or depth sensors to identify vacant spaces.

A conductive wire inspection device uses a reference body to calculate relative position and angle for acceptance determination.

A trained neural network localizes internal bleeding using ultrasound data without ground truth labels.