Image Enhancement TRIZ Cases

Machine learning detects geographic changes to adjust image acquisition frequency and resolution dynamically.

A rendering engine converts medical images into photorealistic anatomical slices using machine learning and atlas references.

Optical copying replaces mechanical simulators and X-rays to record precise mandibular kinematics without radiation exposure.

A state change tracking device uses X-ray Talbot imaging to extract feature amounts from reconstructed images of inspection targets.

Diversity-promoting regularization captures infrequent clinical patterns to reduce overfitting while maintaining model efficiency.

Iteratively adjusting dual camera focal lengths resolves high equipment costs by generating precise disparity maps from standard smartphone hardware.

A lower limb operable multifunctional interface uses timing signals to trigger distinct control actions via a single activation element.

A noise detector scans a blank area and a reference white plate to identify the origin of image defects in an inspection apparatus.

A pulse wave detection device converts RGB frames to YIQ components to isolate skin regions for signal extraction.

A stereo camera device adjusts image cropping position and range based on detected road shape to measure distance accurately.

Automated facial image analysis system generates personalized treatment recommendations using machine learning classification of patient condition severity.

A 3D object detection device calculates movement speed variability from aligned bird's-eye view images.

Depth map quality assessment filters spoofing attacks by validating spatial features before neural network processing for accurate recognition.

Automated cinematic decisions based on 2D pose data resolve the contradiction between dynamic video focus and user privacy concerns.

Crowdsourced segmentation paired with deep learning resolves overlapping chromosome classification bottlenecks.

A deep convolutional neural network calculates a spatial spread parameter from medical imaging data to predict patient survival outcomes.

ML-based alignment evaluation selects robust models to correct SEM image distortions, improving defect detection accuracy in IC manufacturing.

Crop image and supplemental flat field correction map to isolate defects, then scale the map using a cost function to reduce computational complexity.

Optical imaging captures plasma sheath features for real-time substrate processing control.

A single RGB camera captures depth and video data by receiving discontinuous infrared light patterns projected during specific time intervals.

Dual-energy image fusion identifies materials by atomic number, resolving human interpretation errors in security screening.

A patient monitor stores vital sign data alongside ultrasonic image capture timings for synchronized display.

Image processing apparatus prioritizes human body detection in priority areas based on camera installation direction.

An apparatus selects regions of interest and applies high dynamic range processing to reduce brightness differences before stitching multiple images.

Iterative total variation reconstruction minimizes Gibbs artifacts while maintaining resolution.

Comparing image data with sensor measurements compensates for optical distortions and resolves location inaccuracies in remote imaging.

A first processing unit synthesizes raw image data by aligning corresponding pixels from multiple sources to generate synthesized raw image data.

An EUV reticle uses an absorption stack to measure penumbra effects, enabling precise image-error compensation.

Service oriented architecture segments processing tasks into modular components, resolving productivity complexity tradeoffs in geospatial data systems.

A tomosynthesis system detects internal breast tissue motion by analyzing pectoral muscle boundary shifts across projection frames.

A medical image display system enlarges regions of interest within fixed frames while synchronizing movement across multiple similar images.

Exponentially stabilizing control laws adjust optical axis orientation and lens zoom to maintain image centroid position, reducing device complexity costs.

Neural processing unit decodes video bitstreams and feature maps, eliminating separate memory allocation for AI model weights.

Segmented virtual slide panes synchronize biomarker thumbnails with tissue images, eliminating cumbersome manual alignment and reducing diagnostic time.

Image processing system corrects relative position changes between detector and test field to stabilize analyte concentration measurements.

A microfluidic channel generates a cell-free plasma region via rouleaux formation to enable direct analyte concentration measurement.

Steerable filters detect lane lines in grayscale road images, reducing false alarms from changing environments.

A trained convolutional neural network synthesizes bright images from raw sensor data without increasing ISO or shutter speed, avoiding motion blur and noise.

A system places augmented reality items relative to a tracked three-dimensional reference point within video footage.

Tissue composition maps separate calcified deposits from breast parenchyma for precise density measurement.

Direction sensors measure included angles to compute horizontal distances via trigonometry, avoiding expensive laser rangefinders.

A transient improvement circuit filters local overshoot and undershoot at edges to generate a quality-improved image signal.

A color name determination device retrieves relevant images and extracts keywords to map input colors to character strings.

A deep feature flow framework propagates sparse key frame features to intermediate frames via a computed flow field.

A system detects candidate feature points in satellite imagery and triangulates their locations to recommend accurate ground control points.

Machine learning algorithm processes tomographic perfusion data to generate virtual stroke follow-up images.

A tone conversion apparatus maps input levels to output levels using a variable luminance step size based on human visual perception.

A calibration method uses z-stack light-field images to determine total and microlens magnification for precise system mapping.

Segmenting the field of view allows each camera to optimize its projection, resolving the trade-off between wide coverage and distant detection accuracy.

A template generation module creates candidate reference marks to align new slide images with high precision.

Focal plane arrays capture images at distinct integration periods to cancel non-uniformity noise without frequent recalibration.

Automated recoater monitoring detects powder bed defects to terminate failing builds and reduce material waste in additive manufacturing.

A tomosynthesis imaging support apparatus derives body movement features from subject data to estimate motion during acquisition.

Cascaded decoding units restore high-resolution images by applying inverse wavelet transforms and superimposition operations across multiple stages.

Machine learning algorithms detect landmarks to reconstruct annular structures, reducing manual analysis time while maintaining diagnostic precision.

Image analyser processes camera edges to detect fog, activating lights and closing vents.

A detection system processes color data and motion signals to identify tiny objects in image frames.

A neural network generates feature maps from two-viewpoint images to estimate a third viewpoint image.

A region-based method updates representative functions via gradient calculations to generate iteratively regularized partial images.

Computational processing fills missing voxels in medical images using histogram matching and checkerboard algorithms.

Automated image processing converts visual fixture locations into spatial coordinates, eliminating manual measurement delays during indoor commissioning.

Angle-dependent shift corrections process multi-angle captures to resolve longitudinal chromatic aberrations and astigmatism without complex deconvolution.

Segment organs and tumors using evolution equations that evaluate topographical distances and statistical metrics for precise delineation.

A hologram generating device propagates segments using kernels modeled from display aberrations to reconstruct three-dimensional images.

A medical image processing apparatus generates a blood vessel shape model from time-series fluoroscopic images to perform fluid analysis.

A photogrammetry system generates 3D models from images to detect product damage during delivery.

Differential multi-phase simulation estimates contact angles by comparing measured porous medium images with generated flow simulations.

Time-sliced image processing refines intensity profiles and threshold values for accurate lane marking detection.

A closed 3D reference object enables automated measuring system checks through multi-directional recording and distance comparison.

Image processing apparatus corrects projection distortion using homography transformation on captured pattern images.

Morphological filters normalize images to detect real deviations while tolerating registration errors.

A deformation vector field corrects spatial registration errors through automated feedback loops.

Segmenting images by depth resolves exposure inaccuracies in artificial lighting, preserving foreground and background details.

A digital image processing method uses iterative tile operations to reduce noise while preserving visual quality.

A display image compensation module adjusts pixel luminance to maintain visibility during power shortages.

Angled illumination bypasses retinal pigment shielding to reveal choroidal vessels, enabling machine learning analysis of vascular indices.

Segmenting skin images below 200 μm enables targeted cosmetic deposition that resolves the contradiction between concealment capability and natural appearance.

Processor corrects training skeleton data joint distances to match a target person, reducing manual action classification time.

Computes limited optical flow estimates and interpolates synthetic video frames between actual recorded images.

Deep learning models segment organs in CT images, reducing manual annotation time and variability.

Binocular cameras capture depth images during excavator rotation to generate precise three-dimensional models of material piles.

Segmenting background images into independent sub-regions allows dynamic foreground shape determination, reducing time required to regenerate templates.

Convolutional neural networks classify correlated optical and electrical defects in image sensor arrays to automate root cause identification.

A system associates camera image positions with map coordinates to detect individuals and totalize human congestion states alongside movement flow patterns.

Segments image data into low and high frequency blocks to balance contrast enhancement with computational efficiency while preserving local details.

A processor selects Dynamic Texture Scaling, Content Adaptive Compression, or On Device Texture Compression to optimize texture memory.

A velocity map generator synthesizes 3D pixel displacements by combining depth maps with optical flow vectors.

A scraper member removes dust from a rail part before it enters a guide groove, maintaining optical characteristics of the scanning device.

Processing circuitry determines feature point positions in X-ray images to generate superimposed 3D models.

A head-mounted display selects eye images using quality metrics to generate hybrid iris codes.

A differenced change product scales coherence pixel values to highlight human activity in synthetic aperture radar imagery.

Measuring marks in the dummy region enable real-time detection of seal line position and width, preventing misalignment and image deterioration.

A medical imaging apparatus extracts persons from camera images and specifies the subject based on distance to a patient table reference position.

Segmenting images into local regions reduces computational load while maintaining high accuracy for automated measurement point identification.

A pedestrian detection system transforms camera images into a top view to calculate actual height for true positive identification.

A bilateral filter system constructs histograms within rectangular image regions to compute new pixel values efficiently.