Image Enhancement TRIZ Cases

This case combines Kalman filtering with RGB-depth superpixels to track surgical tables and sites despite shadows and lighting variation.

A multichannel imaging pipeline uses single-channel classifiers to improve typing accuracy for rare cells and quantify protein markers.

Perturbed training images help a DCNN detect tool configuration during catheter insertion for more precise instrument control.

An L-shaped calibration card corrects image distortion and color before wound regions are classified as granulation, slough, or eschar.

Wall-and-floor modeling, 3D inpainting, and staged segmentation help control real-world objects with less processing heat and power.

AI templates adapt document images for rotation, distortion, and incomplete views.

Intraoperative 3D reconstruction and non-rigid coregistration align changing soft tissue with preoperative surgical data.

A physics-based color chart approach recovers high-resolution sample spectra under arbitrary ambient light without large training datasets.

Image groups, motion blur, and object segmentation recreate slow-shutter effects without professional shooting tools.

Edge filtering and cloud analysis help DCCN identify object dependencies in real time while reducing bandwidth and local computing demands.

This MRI case uses magnetic sensor arrays instead of complex NMR links to monitor fields and support more accessible clinical imaging.

A coordinated screen links predetermined-area images with 3D position and time markers for clearer wide-field image navigation.

A pre-trained completion model infers structural and color data in undetected regions, producing a more complete robot environment map.

A test bolus and CT contrast curve replace manual timing calculations, setting prep delay for synchronized contrast imaging.

Learn how synthesized interferogram pairs train a GAN to remove phase errors and improve SFQR and SBIR across wafer profiles.

Time-based color filtering helps lighting units match illumination to the day.

The method adjusts fluctuation correction using focal length and object distance, while motion detection protects moving objects from blur.

This case maps luma and chroma with adaptive multipliers to support HDR-to-SDR conversion across varied displays.

The method detects and corrects metal regions in sinograms before tuning projection geometry for more reliable CBCT calibration.

This case fuses features from multiple image resolutions to preserve global context and local detail during denoising.

A dynamic mask algorithm subtracts preceding contrast images to generate clearer, real-time angiographic images during CO2 injection.

Low- and high-brightness pixel filters screen dark scenes and source size to improve point light source detection for HDR imaging.

A composite image and depth map retrieve only the tomographic slice matching a selected region, reducing storage and processing load.

Octree-based isolated-point signaling applies detailed coding only where needed, reducing point-cloud overhead for real-time transmission.

HSV color segmentation and edge analysis automate accurate grain flake thickness measurement, reducing human error.

Infrared depth imaging uses visible-light edge regions to correct boundary errors and improve distance measurement accuracy.

Automatic implant classification from pre-exposure images adjusts mammography parameters to improve image quality and diagnostic accuracy.

Manual input, focus movement, and depth information dynamically set the target focus position for more accurate subject selection.

Neural networks analyze differential angiographic sequences to locate small vascular constrictions.

An orientation-based second rendering highlights tangential surface regions while alpha transparency keeps both anatomical models visible.

Pre-constructed human body models replace real-time multi-angle transmission, reducing network load and improving remote interaction.

A reference coordinate layer guides deep learning to align different fields of view, reduce fused boundaries, and improve image quality.

The case repositions anchor graphs while preserving edge lengths and angles to improve virtual object placement in physical environments.

This case uses reusable motion tracks and dynamic effects to overcome rigid, monotonous augmented reality text display.

When high ISO degrades color, a second camera guides first-image optimization for more objective and robust representation.

Temporal video features improve microscope lesion classification and reduce missed tiny regions.

This case addresses noisy, incomplete XR scene models by fusing color, depth, and cloud processing for scalable 3D object recognition.

A weighted luma and largest-color mapping improves HDR-to-SDR conversion accuracy for legacy and modern displays.

Generate high-depth-of-field images from stereo pairs without time-consuming z-stacking.

Preoperative 3D images define patient-specific tracker markers, reducing line-of-sight loss and obstruction during surgical navigation.

A reflector and holder capture the tool profile in infrared, enabling more accurate pose computation for surgical navigation.

This video surveillance case uses target and reference-zone 3D coordinates to improve intrusion and zone-crossing detection accuracy.

Keypoints and known beam geometries generate masks that remove artifacts and protect patient privacy across ultrasound vendors.

This case combines machine learning with conventional estimation to calibrate dental DVT geometry while preserving boundary conditions.

A pre-trained model detects and tracks lesions in endoscopic images to calculate size information despite operator fatigue.

This case uses density distributions and position tracking to measure crowd flow more accurately when individuals overlap.

A monocular camera uses normalized 3D facial geometry and head pose to estimate gaze across multiple screens in real time.

Automatic fiducial registration flags missed points for manual correction, supporting accurate spine implant navigation.

Attribute-based matching combines mobile and fixed-camera footage while supporting synchronized output and user-approved video sharing.

A deep learning PCCT viewer selects clinical tasks and organizes anatomical and pathological reviews to streamline diagnosis.