Image Enhancement TRIZ Cases

A binary phase filter and neural reconstruction approach extends depth of field while preserving image resolution across multiple depths.

Pre-set imaging conditions let one dynamic X-ray capture support multiple analyses, improving accuracy while reducing subject burden and exposure.

Weighted eye and mouth feature points from normal and smiling face images enable low-cost, non-invasive dementia determination.

Multiple pinholes and stored light-dark patterns enable deconvolution that restores clearer images while keeping the imaging unit compact.

High-frequency features are converted to B-spline representations to cut warping cost and reduce blur, moire, and overshoot artifacts.

Text-guided 3D generation is improved by refining multi-view images with diffusion models before updating a scene reconstruction model.

A slotted filter and movable x-ray source stitch multiple projections into parallax-reduced 3D images for accurate feature detection and implant placement.

Ellipse-based prediction infers full iris and pupil regions from partially occluded eye images, improving gaze and eye-tracking accuracy.

Multiple image degradations are detected and corrected on demand, cutting wait time while avoiding idle-state image processing.

Machine learning removes probe-specific ultrasound variation so anatomical features can be matched across scans and tracked more reliably over time.

A variable image queue compares recent and older cargo-space frames to detect fast and slow object movement in real time without training.

Prioritizing correction by object importance and prevalence keeps image quality consistent across multi-object image sets.

Camera-based pose recognition replaces fixed controllers, enabling freer user movement while executing commands from body gestures and relative position cues.

AI prepopulates radiology reports and keeps image annotations and text synchronized, cutting manual entry, focus switching, and omissions.

A unified 2D/3D segmentation workflow helps constrain regions of interest and improve TTFields array placement planning.

GPS path data is converted into masks and contours to identify field headlands accurately in complex terrain without satellite imagery or user input.

Automatic calibration aligns wide-angle and PTZ cameras from overlapping views by matching point pairs and computing rotation matrices.

Real-time image overlays show object detection accuracy by region, helping installers adjust camera position and rules without repeated trial and error.

Smartphone image processing standardizes blood bag quality checks despite lighting and camera variation, improving objective disposal decisions.

Camera-based pose tracking infers whether a person intends to open a door, improving access reliability while avoiding unnecessary openings.

Machine learning maps in-vivo MRI or photoacoustic tissue images to mechanical responsiveness, avoiding elastography limits and arthroscopy risks.

3D CNN motion extraction, attention weighting, and feature fusion improve video quality scoring accuracy while reducing overfitting.

Adaptive min/max depth projection improves point cloud compression for folded geometries while reducing noise, missing points, and reconstruction errors.

Multiple embedded and perimeter cameras track table play to deliver immersive broadcast views, automatic angle selection, and game analysis.

Color-coded segmentation highlights stained cell aggregates across the full tissue image, helping operators choose analysis regions despite noise.

A CNN-based chromatic aberration model separates brightness and chromaticity to remove edge fringing without color loss or false artifacts.

Direct depth measurement and screen coordinates replace 2D-to-3D estimation, enabling more precise and efficient AR hand interaction.

Single-frame body reconstruction uses image segmentation and texture generation to improve 3D accuracy without complex capture setups.

A camera motion trajectory adds spatial cues to video playback, helping users jump to the right frame when time-based scrubbing falls short.

Laser-scanned 3D maps track swaying hung loads and keep crane guide information accurate, reducing operator burden.

Depth maps tied to navigable map elements compress environmental geometry to deliver sub-meter vehicle positioning for automated driving.

A neural network links product images with known and hidden fabrication parameters to explain quality variation beyond defect detection.

Calibration and dynamic image scaling align 3D vessel images with fluoroscopy, reducing overlay gaps and X-ray exposure during catheter surgery.

Multiple light sources on a translucent carrier add angle-selective illumination in laser scanning microscopes without major beam path changes.

Uses floorplan constraints and structural elements to correct indoor camera pose drift, improving wall alignment and floor smoothness in 3D scans.

Vertical stripe fetching with two K-means pixel clusters cuts memory bandwidth overhead while improving pixel retrieval for image registration.

Parallel ISP processing preserves exposure and noise data, giving neural networks cleaner inputs with lower complexity and fewer errors.

Multiple RGB and depth cameras segment and recompose participants to keep faces visible and reduce obstructions during video conferencing.

Optical and volumetric oral scans are registered to track scanner position without markers, reducing visits, errors, and x-ray exposure.

Image alignment corrects camera motion in infrared sequences, enabling accurate gas flow estimation from a designated area.

Manual OCT probe scanning reconstructs B-mode images by removing correlated A-lines, cutting scanner complexity, probe size, and cost.

Luminance statistics validate image sharpening after abnormal-region exclusion, improving bacteria extraction despite proliferation and foreign matter.

A machine learning model flags likely incorrect image segmentations, reducing full manual review and focusing correction where needed.

Integrated OCT enface and 3D imaging aligns laser delivery and confirms eye targets to reduce tissue damage and device complexity.

Machine learning compares live endoscope images with a built image database to recognize revisited locations and improve navigation accuracy.

Peak intensity and local variation features let streak defects be graded automatically in line with human visual judgment.

Automated CNN image screening filters bad images and verifies true single cells with acceptable morphology for reliable clonal cell line development.

Localized detector-specific filtering sharpens radiographic images, reducing blur and artifacts without extra imaging or radiation exposure.

Depth-based virtual relighting creates reference images for vehicle guidance testing without complex physical light source setups.