Image Enhancement TRIZ Cases

Body-mesh tracking deforms AR external meshes without depth sensors, cutting mobile processing load while improving whole-body effects.

Combining perfusion maps with hypodensity overlays improves detection of irreversible brain tissue damage for treatment decisions.

Non-invasive CT and machine learning quantify coronary plaque and stenosis to guide treatment choices and avoid unnecessary procedures.

Dual-energy tomosynthesis subtraction captures iodine uptake and breast structure in less time, improving lesion localization with lower radiation.

Rough positioning plus pixel clustering and width-length analysis improves LCD screen defect detection, especially for hard-to-see linear flaws.

Latent encoding of vessel frames and ECG signals reconstructs clear vessel sequences while reducing radiation time and contrast medium use.

Combining RGB, IR, and depth images enables accurate profile contour matching under variable lighting without costly calibration or AI training.

Lightweight CNN kernels, distillation, and multi-task sharing enable real-time GI endoscopy lesion detection and quality control at the edge.

A deep gradient prior guides one-step L0 smoothing to sharpen images while reducing computation and preserving edges and natural details.

A projective scaling check screens distorted text fields before OCR, rejecting restorations too weak for reliable recognition.

Multiple differently oriented optical sensors and image processing replace gyros to maintain spacecraft position and rotation data under disturbances.

CNN-scored sub-spheres and optimized rotation matrices crop volumetric video by viewing angle, reducing storage and easing VR navigation.

Aligned images and displacement-based segmentation reveal regional lesion change, supporting tumor, inflammation, and vascular analysis.

Real-time AR prompts keep surgical instruments within the optimal recognition range, avoiding distance and lens distortion errors.

Tiered scoring and enhanced scrutiny flag high-risk drug packages for stricter thresholds or manual review to reduce dispensing errors.

Video tracking of anatomical regions measures motion parameters to identify bradykinesia behaviors and support more precise therapy adjustment.

AI segmentation, radiomics, and multitask models quantify COVID-19 lung tissue from radiological images for more reliable classification.

Confidence-triggered data augmentation helps target tracking handle data drift, improving positioning accuracy and robustness with less unnecessary processing.

A trained model combines catheter images with instrument position data to separate tissue and tool regions for faster, more precise intervention guidance.

Extracts gum bone lines from oral images over time to quantify tooth-level periodontitis deterioration and display changes clearly.

AI segmentation of CT and other medical images improves body composition measurement accuracy and enables tracking of tissue changes over time.

Temperature-based PSF selection corrects coded imaging depth errors caused by optical shift, improving accuracy without heavy recalculation.

A camera-generated virtual dental scene overlays the real view to improve real-time treatment analysis, simulation accuracy, and patient understanding.

A hierarchical variational autoencoder restores 4K and 8K image details from low-resolution inputs while saving storage and adapting to devices.

A 3D vascular model displays local and distal FFR together, improving CAD assessment speed and precision for revascularization planning.

By adjusting scan depth line by line to match a segmented anatomical region, 3D ultrasound improves frame rate without losing coverage.

Color-coded slide comparison and user-triggered rescanning help isolate scan discrepancies and speed image correction.

Thermal imaging extracts live-stream contours for real-time AR effects, simplifying synthesis and avoiding large green screens.

Capturing multiple die alignment marks in one image cuts alignment time and processing load while preserving precise mark identity and location.

Camera-based image analysis replaces manual color tuning to correct module-to-module brightness and color variation with less calibration time.

Rescaling picture dimensions before neural downsampling cuts bitstream size while preserving reconstruction quality and limiting information loss.

Combining image and time-series models helps endoscope support tools locate lumens more reliably when views are obscured or image quality drops.

Object and road attributes narrow IMM tracker models, cutting processing time while keeping vehicle object tracking aligned with real motion.

Overlapping boundary segments improve deformation imaging accuracy for mechanical dispersion and cardiac dyssynchrony assessment.

A built-in camera and motion tracking sensor fuse real-time view and position data to improve instrument awareness in minimally invasive surgery.

Batch interpolation and register-based pixel sets raise distortion correction throughput while cutting SRAM demand and circuit area.

Multiple angiographic segments are stitched into one vessel roadmap, enabling accurate co-registration of intravascular data across long peripheral vessels.

Intermediate-layer feature clustering separates overlapping semiconductor defects, improving classification accuracy and reducing inspection time.

Identifies ablation antennas in CT by combining skin entry point, trajectory analysis, and radiopaque markers for clearer navigation.

Radar backscatter, elevation, and sun angle are used to predict visible-infrared Earth images when cloud cover blocks optical sensing.

Varying RAW processing by file type preserves image data and keeps image quality consistent across different development applications.

A sandboxed secure data vault detects and blurs private spaces in AR/VR camera feeds before sharing, reducing privacy exposure in real time.

On-chip DCT compression of raw mosaiced frames cuts off-chip DRAM use, lowering power while preserving flexible high-quality video capture.

Stereo image disparity builds a road baseline model to detect small path hazards on non-flat surfaces with lower cost and fewer false positives.

Machine learning generates mirrored photorealistic 3D fashion assets for XR, cutting manual image creation time, effort, and equipment cost.

Alternating image rows or columns with supplementary pixel data enables single-camera 3D point cloud capture with lower hardware demand.

A camera and motion tracking sensor share one surgical instrument to correlate view and position in real time for more precise minimally invasive surgery.

A confidence map steers ray tracing to unpredictable pixels, cutting frame delays while preserving visual quality in cloud gaming.

Epipolar-based 2D-to-3D reconstruction improves intraoperative image registration and surgical guidance without separate imaging workflows.

Weak 2D-3D cross-attention cuts per-point annotation effort while improving semantic segmentation of texture-poor 3D point clouds.