Image Enhancement TRIZ Cases

Different display indicators separate nearby detected subjects, making target selection clearer for AF, exposure, and white balance control.

A cGAN generates alternate-class images and cleaner explanation masks, reducing saliency noise and improving trust in AI classification.

Orientation sensor data is matched to 3D body-surface normals to locate a treatment device accurately without extra worn sensors.

Neural point cloud completion fills sparse building data before 3D reconstruction, improving model accuracy and structural integrity.

Motion-compensated microbubble localization improves contrast-enhanced ultrasound resolution while reducing residual clutter at conventional frame rates.

Pre-restored channel images guide joint demosaicing and denoising to cut false artifacts, suppress noise, and preserve edge detail.

Manual OCT probe scans are converted into B-mode images by removing correlated A-lines, avoiding complex mechanical scanning hardware.

Field-strength and iron correction convert dual MRI acquisitions into standardized cT1 maps across scanners for reproducible treatment decisions.

By combining print image data with article surface texture, this case generates realistic product previews that better match finished color and feel.

Calculating gray values only at fiber centers and interpolating the rest speeds microscopy image reconstruction while removing grating residues.

Text-encoded location constraints guide diffusion denoising to place and size objects more accurately in generated images.

Transforms medical images with alignment data to predict standing bone poses during surgery, reducing fluoroscopy use and radiation exposure.

Relative-position matching triggers content within preset ranges, enabling private indoor and outdoor placement without real map data.

A trained correction model generates an array-matched coefficient matrix to remove inter-element crosstalk artifacts across imaging systems.

Multiple point clouds and orientation checks help a mobile depth sensor reject multipath artifacts and improve object dimension accuracy.

Optical flow vectors in endoscope images reveal contamination or damage early, supporting reliable imaging and timely maintenance during surgery.

Multi-layer CNN masks limit mammography and tomosynthesis computations to the breast region, cutting runtime without losing diagnostic accuracy.

A Transformer model predicts future target motion from prior breathing cycles, enabling real-time radiotherapy beam adjustment with tighter margins.

B-mode clips are converted to M-mode images so pleural line detection and CNN classification can rapidly flag absent lung sliding.

Portable microscopic imaging and AI analysis identify bacteria on everyday objects without bulky microscopes or specialist skills.

Joint filtering across imaging phases preserves intensity and sharp edges while reducing noise, enabling lower-dose multi-phase scans.

Pre-estimating moving targets during manual PTZ tracking enables one-action automatic tracking with less operator workload and fewer missed targets.

Surface camera images are fused with DIBH CT data to map free-breathing contours for radiotherapy planning without a second CT scan.

Wheel coordinates, 2D box data, and vehicle angle are combined to recover accurate 3D vehicle bounds from incomplete images.

Unequal RGB depth-plane allocation in stacked waveguides improves AR/VR depth perception while reducing waveguides, artifacts, and compute load.

Continuous sensor monitoring and AI-guided dosing adjust chemical stimulus and neuromodulation at symptom onset to improve mobility.

Transverse wave-shaped pixel packets embed a hard-to-copy security feature in personalized images and enable forgery checks by frequency analysis.

Motion trajectory data separates closely spaced objects into distinct real-time position markers, avoiding merged tracks in smart home spaces.

Static content detection triggers low-power display messages and pixel refresh to reduce burn-in risk and extend medical display life.

A pre-trained AI model compensates for optical fiber attenuation to determine fluid hue accurately without manual calibration.

High-resolution scans combine orthomosaics, 3D surface models, and pixel labeling to detect ground damage and produce precise reports.

Builds a 3D object trajectory from unsynchronized camera views to overcome occlusion and improve real-world position detection.

CNN analysis of overlapping vehicle camera views detects duplicate objects and corrects twin-effect artefacts for more reliable stitched images.

Multiple scenario-specific AI models lighten ISP computing load while improving image processing speed and quality in electronic imaging.

Generated defect images let article inspection units verify image processing and quality judgment during production without stopping the line.

Asynchronous keyframe CNN processing and optical flow tracking enable real-time mobile image effects without processing every video frame.

Histogram-guided cubic spline mapping adapts image luminance to different display dynamic ranges while preserving levels and contrast.

Multiple imagers with lens stacks and near-IR capture are combined to improve low-light sensitivity, dynamic range, and image resolution.

Separates overlapping cargo and vehicle structures in X-ray images using adversarial decomposition to improve inspection accuracy and speed.

Infrastructure and onboard sensing are combined to update camera rotation matrices and keep vehicle calibration accurate under dynamic factory conditions.

Separating moving ROI position markers from fixed type labels helps endoscope image overlays stay readable without obstructing observation.

Facial recognition and 3D scanning replace fiducial markers to keep reconstructed images aligned during patient movement.

Ultra-short echo MRI with ferumoxytol quantifies and localizes blood-brain barrier disruption more reliably than DCE-MRI.

Angled imaging of tube ends quantifies axial runout on a μm scale without contact, reducing damage and fracture risk in glass tube production.

Refined edge maps and row intensity differences help detect stable scrolling text regions in video while reducing false positives and artifacts.

Pixel-wise quantization-aware input helps a deep learning loop filter improve reconstructed image recognition and video coding efficiency.

Functional brain activity is mapped onto patient-specific 3D anatomy to automate target selection and safer neurosurgical trajectories.

Registers 3D skeletal data with selective 2D x-rays and optical images to guide tools accurately while reducing radiation and CAS complexity.

Connected component clustering isolates address blocks and barcodes in binarized images, cutting OCR time and compute load while preserving reading accuracy.

Relative oxygen-saturation color mapping makes normal and ischemic tissue boundaries clearer during endoscopic surgery, helping reduce suture failure.