3D Imaging System for Damaged Negative Surface Reconstruction
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Solution Overview
Problem
Conventional methods for digitizing and restoring damaged photographic negatives, such as those deteriorated by chemical reactions, are labor-intensive, time-consuming, and costly, especially when dealing with large collections, as they require physical restoration and do not lend themselves to efficient digital or virtual reconstruction.
Innovation Solution
A 3D imaging system using high-dynamic-range structured-light scanning and Gaussian models to estimate pixel depth and surface reconstruction, incorporating Principle Component Analysis for photometric error correction, allowing for the virtual reconstruction of workpieces by analyzing light transmission and reflection, enabling minimal human intervention and efficient digitization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physical restoration methods are used to restore damaged photographic negatives, then restoration quality is improved, but time consumption and labor intensity increase significantly
Solution Approach 1:
The patent replaces the mechanical physical restoration process with an optical imaging system. Instead of manually separating and reseating emulsion layers, the system uses structured light projection and photogrammetry to capture and reconstruct the negative's surface topology, substituting mechanical manipulation with optical measurement and computational processing.
Solution Approach 2:
The patent creates a digital 3D copy of the damaged negative's surface through structured light scanning. This virtual replica captures the wrinkle patterns and surface deformations caused by deterioration, allowing digital analysis and restoration planning without physical handling of the original artifact.
2Reliability
If physical restoration methods are used to restore damaged photographic negatives, then restoration quality is improved, but cost increases due to labor intensity
Solution Approach 1:
The patent replaces expensive manual restoration labor with an automated optical system. The structured light scanner and computational algorithms eliminate the need for skilled restoration artists to physically manipulate fragile negatives, significantly reducing labor costs while maintaining restoration quality through objective measurement.
Solution Approach 2:
The system enables self-service restoration analysis by automatically capturing, processing, and analyzing negative surface deformations without human intervention. The computational pipeline independently calculates wrinkle patterns and surface topology from the captured images, eliminating manual inspection and documentation steps.
3Productivity
If conventional digitization techniques are used on deteriorated negatives, then digitization speed is improved, but measurement precision deteriorates due to large channels and wrinkles
Solution Approach 1:
The patent transitions from 2D flat scanning to 3D surface measurement by projecting structured light patterns and capturing their distortion by the negative's surface wrinkles. This adds the depth dimension to the digitization process, allowing accurate measurement of surface topology including large channels and wrinkles that conventional 2D scanning cannot capture.
Solution Approach 2:
The patent changes the measurement parameter from flat image intensity to 3D surface geometry by analyzing the distortion of projected light stripes. This parameter transformation enables precise measurement of surface deformations caused by deterioration, converting the problem of wrinkles and channels into measurable geometric data rather than image artifacts.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively reconstructs the 3D surface of damaged workpieces like negatives with high accuracy and efficiency, reducing the need for physical restoration and minimizing human intervention, thus addressing the limitations of conventional techniques.
Implementation Method 1
displaying and sweeping multiple light stripes in a first directional orientation across each pixel of the display screen
Implementation Method 2
determine a difference in a width and a profile of the multiple light stripes caused by the workpiece as light from the multiple light stripes is transmitted therethrough
Implementation Method 3
A Principle Component Analysis is then used to estimate the photometric error and effectively restore the original illumination information
Data Source
AI summary
To obtain a three-dimensional virtual reconstruction of a workpiece the workpiece is positioned on a display screen between the display screen and at least one imager wherein the imager acquires multiple images of the workpiece while (a) multiple light stripes are displayed and swept in a first directional orientation across the display screen, (b) multiple light stripes are displayed and swept in at least one second directional orientation across the display screen, and (c) multiple images for each position of the multiple light stripes at different exposure times are captured. From the multiple images, a difference caused by the workpiece in a width and a profile of the multiple light stripes is determined. That difference is used to calculate a depth value (z) of the workpiece at each imager pixel position (x, y). The calculated depth value is used to reconstruct a surface shape of the workpiece. In embodiments, the described transmittance light capture analyses are supplemented with reflectance light capture analyses.