Image Enhancement TRIZ Cases

This case uses spatial and temporal fluorescence gradients to mark blood flow direction on surgical images in real time.

This case uses product-image parameters and market-success modeling to guide mobile photography and improve image quality.

A second camera prepares encoding and exposure before an object enters view.

Geometry extraction and data fusion combine sensor inputs to reconstruct interior and exterior features, including roof damage.

Acquisition parameters guide a deep neural network to correct variable blur, producing sharper, lower-noise medical images.

AI-guided imaging decisions use clinical context to reduce follow-up delays.

Connected images across focal planes analyze lens effect and refractive index to improve cell viability determination without staining.

A choroidal vascular image separates vessel structures from fundus images before watershed detection, improving network analysis.

A neural network fuses dual-camera and dual-pixel depth maps to improve accuracy across object distances and guide background blur.

Automated CAD detects microcalcifications and masses, combining them with patient history to improve risk scoring and follow-up.

This case uses encoded metadata and luma-guided filtering to upsample HDR chroma, aligning it with SDR quality across devices.

A single angled C-arm image uses known fiducial poses to register anatomy while reducing radiation exposure and procedure time.

A semi-supervised framework uses embedding features and unlabeled video sequences to correlate instances across frames.

This case compares chip images, RFID data, and game outcomes to expose overlap-related errors and suspicious casino betting patterns.

Pressure sensing and kinematic modeling visualize sleep movements while reducing video storage, processing time, and computational load.

A digitally tilted composite image merges needle segments across CT slices to show entry point, trajectory, and tip position.

Legacy seismic and well-log rasters need manual review; paired ML denoising and segmentation automate curve extraction for analysis.

A laser and wavelength-matched camera filter isolate smoke motion in video, improving detection reliability across large monitored spaces.

A learnable texture extractor and cross-scale attention module improve reference-texture transfer while limiting blur and distortion.

This case selects multiple or fixed prediction modes by attribute complexity to improve 3D point cloud compression efficiency.

Automate reference image registration and sequence print inspections after job completion.

Selects compatible material data formats for correct virtual viewpoint image generation.

Skeleton thinning and contour comparison improve defect detection in complex printed characters.

Feature point transforms enable liveness checks on resource-limited edge devices.

A small set of pre-labelled 3D coordinates guides template volumes that label object data quickly for AI training.

Camera and X-ray data enable patient-specific chest rotation assessment, improving alignment checks and reducing avoidable re-scans.

Expected-range tracking filters insignificant motion in bronchial videos, helping evaluate cilia clearing ability with less manual effort.

A normalization device standardizes CT images so AI can quantify plaque composition and improve cardiovascular risk assessment.

A spiking twin network converts asynchronous event streams into millisecond frames for accurate, low-latency tracking in dynamic scenes.

The device overlays guidance on captured finger images and updates imaging until the prescribed position is reached.

Stored image blocks support pipelined CNN processing, reducing DRAM bandwidth needs as larger input images are handled.

Feature-vector editing and pose conversion feed a GAN to expand face databases with varied images for recognition.

Algorithmic PCB file analysis separates physical from auxiliary features, enabling faster outline approval and accurate manufacturing.

The case combines focus movement detection with depth information to match autofocus targets to the user's intended subject.

A global alignment phase and ScaleMapLearner refine monocular depth into dense metric predictions across diverse environments.

Object detection, re-projection, and duplicate suppression enable real-time counting across ultra-wide scenes with one camera.

A shared central clock synchronizes screen refresh and subject-image capture at integer-multiple frequencies, reducing temporal uncertainty.

Estimate tissue section thickness from shallow- and deep-focus images.

A color-gradient composite image lets a convolutional neural network detect gestures while reducing processor, memory, and power demands.

A server varies image-fetch rates for medical device controllers, reducing OCR costs and congestion while reporting alarms promptly.

This case uses a virtual medical element and depth maps to size and position a physical mesh for contoured anatomy.

A learning model maps principal-point likelihoods into a heatmap before calculating camera parameters for more accurate calibration.

Low- and high-resolution imaging, correction, and segmentation improve tissue analysis while reducing storage and computing demands.

The processor generates comparison video from two setting conditions, helping users spot image-quality mismatches with an external recorder.

Automatic image alignment uses periodic design features to register semiconductor inspection images.

This ex vivo workflow images, sorts, and dispenses tissue fragments before drug testing to improve consistency and predictive accuracy.

Separate panoramic and inspection regions use index bars to locate deformations without cluttering the display.

This case combines captured and uploaded images with user selection to replace rigid templates and enrich special effect displays.

Line-scan autofocus, optic nerve head detection, and multi-modal capture support clearer retinal imaging for photophobic patients.

This case uses first and second motion-compensated regions with a neural network to improve prediction accuracy and compression efficiency.

Particle filter tracking estimates head contours for gesture detection, replacing complex button interfaces with natural movement input.

An image segmentation apparatus calculates an adaptive field-of-view function to generate patches matching target sizes.

Source modality latent domain learning aligns EEG, fMRI, and facial movement features to predict cognitive states despite scarce labeled multimodal data.

A preliminary 3D model incorporates bulk density and thickness parameters to simulate radiography data for accurate patient-specific reconstruction.

A robotic system fuses 2D and 3D image data to locate vehicle brake levers for precise manipulation.

Bayesian inference estimates vibration source distance from optical signals, eliminating complex wave propagation modeling errors.

Software processes smart target card images to generate correction profiles, eliminating manual spectrometer measurements and reducing calibration time.

Segmenting wafer regions with local quality thresholds resolves the contradiction between detection sensitivity and false positive rates.

A multi-camera system exchanges feature information between control apparatuses to track a predetermined subject across different capture directions.

A controller masks vehicle camera images with patterns and converts them into multiple views for deep learning.

Detecting transparent obstacles via reflective disparities eliminates redundant mechanical sensors, reducing UAV weight and complexity.

Processor selectively combines auto-white balance data based on detected shadows and steering angle to resolve color variability in surround view systems.

A neural network system extracts stylistic elements from source images to modify target scenes while preserving structural geometry.

A signal processing device calculates a dynamic blending ratio to mix large-pixel and small-pixel RAW signals.

A computer-implemented method constructs a gaze vector using an artificial neural network to locate the point of gaze on a screen.

A cognitive medical treatment system simulates outcomes using models that account for impacts of current therapies on future approved treatments.

A data processing method segments real-world outfit images and fills virtual object wearing regions to enable rapid avatar customization.

Separates material attenuation by setting distinct radiation energies for each component, improving contrast and reducing noise in fluoroscopic imaging.

A route selection assistance system extracts multiple anatomical pathways from patient images and assigns rankings based on delivery ease.

A camera pose tracking method registers data using points and planes for accurate positioning.

A rotation processor converts input images to temporary formats within fixed processing limits.

A block-based image restoration method estimates global motion for segmented regions to improve segmentation accuracy and processing efficiency.

Iterative tomographic reconstruction updates atomic number and density for volume elements to segment samples based on physical properties.

A pattern inspection apparatus applies multi-directional spatial filters to extract precise outline pixel candidates from semiconductor wafer images.

Generates clear cell contours by subtracting defocused bright field images, bypassing phase microscopes and in-focus determination.

Chamfer loss measures agreement with unlabeled depth data to resolve the trade-off between annotation time and detection accuracy.

A video camera identifies fingertip locations in frames to map movements as virtual user inputs, avoiding expensive touch displays.

A patient monitoring system calculates rigid transformations using stereoscopic cameras to align 3D surface models with target wire meshes.

Gradient-based visualization reveals which input pixels drive classification decisions, resolving the black box limitation in semiconductor defect detection.

Segmented base and secondary stations enable remote pest detection via wireless imaging, eliminating exposed wires that cause installation failures.

A deep neural network generates synthetic images by creating depth maps and point cloud representations for accurate perspective transformation.

Electronic device estimates food mass and nutrients via camera image analysis using a reference object for scaling.

A dual camera optical image stabilization system uses a Hall sensor to measure lens displacement for precise image bias correction.

Bidirectional LSTM networks capture sequential context along vascular branches, resolving the trade-off between algorithm complexity and loss of information.

High-speed CMOS imaging captures microbubble motion while watershed segmentation isolates overlapping pixel blocks to extract size and velocity data.

A calculation unit computes image deformation while a display control unit shows progress for user correction of corresponding points.

Multi-view video system estimates camera parameters to reconstruct time-series 3D models despite moving subjects and non-fixed cameras.

Digital inpainting replaces calcified regions in X-ray images with extrapolated background data to derive quantitative measures.

Laser scanning creates digital point clouds to determine obstructed film cooling hole geometry, reducing restoration time and cost in gas turbine maintenance.

Aggregating temporally adjacent neural pose graphs synthesizes corrected poses, resolving accumulated drift errors in SLAM localization.

A correction unit decreases high frequency components for still pixels near motion areas.

Processing unit calculates pixel values before color interpolation to prevent blurring and maintain sharpness in light field composite images.

Deferring abnormality detection until after the examination reduces processing load while maintaining diagnostic reliability.

Cloud system reduces camera array complexity and data processing load by identifying stitching errors in initial renders and applying targeted corrections.

Adaptive patch selection via threshold curves resolves suboptimal noise reduction caused by inaccurate motion compensation in conventional algorithms.

Error correction modules model linear shifts between inertial and vision data to maintain navigation updates during image occlusions.

Segmented structure definitions enable precise anatomical localization across time points, resolving precision issues from patient positioning variations.

Segmenting matrices into submatrices mapped to correspondence tables reduces memory requirements and processing time by limiting search spaces.

A trained AI model segments user faces to generate personalized cartoon avatars with accurate facial features.