Digital Media Enhancement Using Reference Image Extrapolation
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Solution Overview
Problem
Conventional digital media enhancement devices are limited in their ability to accurately remove obstructions or noise from images and audio files, as they lack knowledge of the underlying content behind the obstructions or noise, leading to inferior results.
Innovation Solution
A digital enhancement system that uses a computer-based approach to identify target objects in primary images, search for reference images with similar or identical objects, and modify the primary image by extrapolating data from these reference images to enhance or remove obstructions, while considering copyright and licensing issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pixel replacement methods are used to remove obstructions, then the process is simple and fast, but the result lacks accuracy because actual knowledge of what is behind the obstruction is not used
Solution Approach 1:
The system performs preliminary actions by searching for and analyzing reference images before modifying the target image. Multiple reference images are pre-processed and analyzed to extract background information that will be used to accurately reconstruct obscured regions, ensuring high accuracy before the actual modification occurs.
Solution Approach 2:
Reference images serve as intermediaries between the obstruction and the underlying content. Instead of directly guessing what is behind an obstruction, the system uses reference images containing similar scenes without obstructions as mediators to transfer background information to the target image, achieving accurate reconstruction.
2Measurement precision
If noise-cancelling filters are applied to audio files, then noise removal can be performed, but actual knowledge of the audio without noise is not known leading to limited effectiveness
Solution Approach 1:
The system creates copies of clean reference audio segments and uses them to replace noisy portions of the target audio. By copying verified clean audio from reference files that contain the same or similar content without noise, the system can restore original audio content with high accuracy without losing information.
Solution Approach 2:
The system employs feedback mechanisms by continuously comparing the enhanced audio against reference audio and iteratively refining the noise removal process. The reference audio provides feedback on what the clean signal should sound like, allowing the system to adjust and improve noise cancellation accuracy while preserving original content.
3Manufacturing precision
If reference images are searched and analyzed to enhance the primary image, then the quality and accuracy of enhancement is improved, but the processing time and computational resources increase
Solution Approach 1:
The system applies partial action by selectively processing only the regions of the image that contain obstructions or require enhancement, rather than processing the entire image. By focusing computational resources on specific problem areas and using reference images only where needed, the system maintains high quality enhancement while reducing overall processing time.
Data Source
AI summary
Aspects are disclosed for enhancing digital media. In an aspect, a target object in a primary image is identified, and reference images that include the target object are located. The target object is then modified within the primary image according to data derived from analyzing the reference image. In another aspect, a primary file is received, and at least one reference file is referenced to generate enhancement data that facilitates enhancing the primary file from an extrapolation of the reference file. In yet another aspect, media files corresponding to a common event are aggregated, and a desired enhancement of a primary file is identified. Here, the desired enhancement corresponds to a modification of an obstruction included in the primary file. A reference file which includes data associated with the desired enhancement is then referenced, and the obstructed data is modified based on replacement data extrapolated from the reference file.