Geometric Image Transformation TRIZ Cases

Backside illumination uses sub-bandgap transmission and two-photon absorption to image ICs without thinning the substrate.

Existing 3D radiological volumes generate annotated projections, reducing manual labeling and improving AI robustness to artifacts.

The controller compresses external images for lower delay while preserving higher-volume internal imagery for clear monitoring.

This case adapts an image model with a fine-tuning plug-in to generate accurate content at varied resolutions with lower computational cost.

A latent grid and machine-learning decoder reconstruct texels at varied resolutions without storing full texture pyramids.

This case segments folded print data into selectable areas, enabling separate templates with resizing and positioning adjustments.

A self-orienting map aligns the target area in a predetermined direction, reducing compass-symbol clutter on limited screens.

Aligning image textures before DCT reduces redundant coefficients and improves compression.

Shared-IP camera modules create heavy processing loads; FPGA segmentation scales, combines, and transmits feeds efficiently.

High-priority pixels travel uncompressed while low-priority data is compressed and regenerated by a pre-trained GAN.

This case uses resolution pre-processing and DNN upscaling to reduce bitrate while restoring images for target displays.

This image processing case uses spatial and channel attention to emphasize regions of interest and preserve details for downstream tasks.

A learning model combines detailed narrow-area images with a broad image to limit information overload and improve estimation accuracy.

This case uses segmented minimal 3D geometry, light estimation, and ray tracing to create photorealistic image augmentations.

This encoding approach uses pixel-block interest levels to limit offset compression in low-contrast regions and control bitrate.

A mixed-precision CNN uses temporal data to render at lower resolution, improving image quality while reducing render time.

Pixel attention, depthwise separable convolution, and late upsampling improve PSNR and SSIM while reducing MACs for real-time processing.

This case normalizes principal component coefficients by standard deviation to reduce computation while limiting super-resolution artifacts.

A first AI model identifies image content, then a specialized upscaling model improves quality while limiting computation time.

This case uses transformed raw images and self-supervised feedback to improve vector output consistency and recognition accuracy.

This case maps pixel locations to world coordinates for crop counts and spot-spray prescriptions without transforming or stitching images.

A second processor renders at lower resolution first, then reads higher-resolution data for smoother, cooler image output.

This case separates prediction blocks into DC, low- and high-frequency components to reduce lenslet image data volume.

Third-frame pixels restore out-of-frame references for more accurate image prediction.

This case replaces bulky pan-tilt hardware with virtual lens motion, sensor fusion, and reprojection for stable panoramic video.

Position sensing and adaptive trajectory smoothing stabilize video while reducing computation and preserving the user's intended motion.

Distal sensors capture separate image areas, while processing combines them for high resolution without repositioning the endoscope.

Deep-learning fingerprints expose near-duplicate NFTs and authenticate rarity.

A machine-learning interface uses simulated cardiac electromagnetic outputs to locate arrhythmia sources and reduce invasive mapping needs.

Rotate neural network weights to analyze tilted camera images without preprocessing.

A mixed low-precision CNN and auxiliary motion vectors guide temporal accumulation to reduce TAA ghosting in low-resolution rendering.

The client terminal enhances compressed game video in partial images, reducing delay while preserving real-time display quality.

This rendering approach upsamples low-resolution light-probe irradiance maps to reduce mobile computing load while preserving image quality.

Scale factor inputs let one SR neural network serve multiple resolutions, reducing memory, processing complexity, and training time.

This case converts images into 3D and varies their view by an external device’s direction and distance.

This case uses spatial-uniformity weighting to reconstruct visible components, reduce infrared noise, and improve RGB-IR image fidelity.

Vehicle feature points define the plate tilt range, enabling digital correction before character recognition.

Document and image analysis automatically sizes inserted images, preserving aspect ratios and relevant content on small screens.

The decoder reconfigures MPM information and projection formats to improve 360-degree image compression for VR and AR data.

This image decompression approach uses origin values and integer difference decoding to balance compression ratio, data loss, and GPU power.

This case combines a tangent fisheye lens and spherical screen to deliver 720° panoramic presentation with faster image generation.

This image processing approach lowers high-resolution rendering power and heat by using temporary buffers for heavily rendered scenes.

A unified network estimates a noise map and adjusts intermediate features to improve super-resolution under unknown image degradation.

A hybrid graph-image architecture preserves spatial patterns and relationships for more accurate circuit routability prediction.

Sender-side content adjustments and render metadata improve text legibility when shared screens are viewed in VR.

This case reconfigures most probable mode information during 360-degree decoding to improve compression for large VR and AR image data.

This case partitions 360-degree images by projection and region to balance decoded quality with image-processing efficiency.

This case reconfigures MPM information and handles projection boundaries selectively to improve 360-degree image compression.

A selected display area produces a brush picture, then neural enhancement or vector conversion improves its definition.

Pixel blending expands gaming displays across surfaces for immersive engagement.