Image Enhancement TRIZ Cases

Channel, segment, and slice dropouts diversify target features, while aggregated predictions improve tracking through occlusion.

High- and low-energy X-ray images receive tissue-based weights to suppress bone visibility and improve lung tumor contrast.

Multiple AI experts learn institutional and personal contouring preferences for more consistent radiotherapy image segmentation.

This case segments grayscale video frames, orders the regions, and colors them sequentially to create diverse, engaging effects.

This case maps floor plans into graphs and uses cloud anchors with AR fingerprinting to route users indoors.

The method locates a reference feature, infers document edges, and crops images without extra captures or transmissions.

Optical inspection images become clustered feature vectors to predict assembly defects in real time and guide targeted inspections.

A shield member blocks cross-light between closely spaced sources and lenses, preserving light distribution in compact range-image devices.

A pretrained keypoint network uses object edges and synthetic models to reduce labeled data needs and processing time in pose estimation.

Dual fixed-position optical sensors compare substrate images to prevent tracking drift during precise printer ink deposition and cutting.

A dynamic vision sensor identifies movement so static regions compress more heavily while neural networks retain critical image detail.

Camera fusion selects the next tracker and sends distance warnings to support covert tracking without target awareness.

Integrated orthogonal imaging improves prostate volume accuracy and patient comfort.

Machine learning screens pathology images for prerequisite conditions, helping target useful tests and reduce unnecessary cost.

This case trains a CNN and FCN with a pose-and-data-volume loss to preserve alignment accuracy while reducing feature transmission size.

The case switches from neural estimation to conventional processing when reliability criteria are not met, balancing speed and accuracy.

This case combines current and previous frames with noise maps and variable weights to reduce noise and expand dynamic range.

Machine learning segments garments from standard camera images, enabling voice-driven AR overlays while reducing hardware complexity.

Automatic region expansion and reduction streamlines annotation while preserving boundary precision with fewer clicks.

Explore LED modules that balance color irradiance and switching noise for reliable code detection on high-speed recycling conveyors.

Mixed reality overlays guide implant positioning and bone preparation during orthopedic surgery.

Integrated X-ray and ultrasound imaging fuses pre-removal contours with specimen 3D volumes for quantitative tissue margin assessment.

Reference-image positioning guides substrate edge processing for greater precision.

Knowledge distillation and microdosing reduce decoder memory and speed decoding for low-bitrate video while preserving visual quality.

Contrast-filled and dye-free image sets localize coronary vessels, reducing pixel analysis for velocity and fractional flow reserve.

Multiple camera poses build a 3D anatomy model, reducing manual setup and improving alignment in surgical navigation.

Iteratively update camera position and environment dimensions from tracked human paths to produce accurate stationary-camera layouts.

Camera imagery identifies stop marks and calculates vehicle speed without sensor synchronization.

SLAM and floorplan feature matching locate captured images and build an immersive model where GPS or RF signals are unreliable.

A plated bonding member strengthens electrode joints while its protective layer improves corrosion resistance in light-emitting modules.

Patch-based PSF estimation and adjustable thresholds reduce manual inspection while aligning image selection with user preferences.

Knowledge graphs improve object detection when overlapping objects reduce classification accuracy.

A 3D model identifies recommended cross-sections, reducing manual setup and improving measurement precision for closure procedures.

Adjustable LED flash timing replaces slow-recharging flashtubes, improving launch monitor flight-parameter measurement.

A supervisor network scores and selects granular motions for each action, reducing the need to train motion networks for every possibility.

Historical motion sequences reveal ice floe changes, improving route planning through dynamic marginal ice zones.

Compare image dimensions and irradiance across sensor distances for fast, interference-resistant range finding in vehicles and robots.

Semantic 3D representations separate walls and objects to improve live previews and final indoor floorplan measurements.

Camera pose and landmark maps set light power and timing for reliable feature detection with lower energy use.

A learned geometric transformer adds orientation to teaching data, reducing manual annotation and improving alignment for inclined objects.

This image processor combines sparse LiDAR depth with frame differences to generate dense, accurate depth maps.

Sequential light levels fuse HDR retinal images, expanding portable fundus imaging to wide fields without pharmacologic dilation.

This case uses sectioned imagery and 1D intensity profiles to detect curved crop rows with lower computational overhead.

Sequential image frames reveal airflow-driven feature shifts, helping visible cameras reduce false alarms from reflected fire.

Image processing and machine learning align selected 2D drawings, reveal object clashes, and support faster conflict resolution.

Cameras identify patients while wearables provide vitals, enabling live overlays for faster assessment in waiting and triage areas.

CBCT builds updated 3D anatomy models for lung catheter navigation, addressing CT-to-body divergence and reducing imaging exposure.

This case projects 3D point-cloud features to the RGB plane, then fuses separate streams for more accurate unsupervised inspection.

Separate structural and material feature paths preserve SE surface detail and BSE shadow information while reducing synthesis artifacts.

This case selects interior or exterior point cloud views from available or estimated normal vectors, improving information visibility.