Image Color Correction via Laplacian Matrix Spill Removal
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Solution Overview
Problem
Current methods for correcting light spilling from backgrounds in blue-screen or green-screen photography do not provide satisfactory results, leading to miscoloration of subjects in images.
Innovation Solution
A method and apparatus using a Laplacian matrix representation of images to determine and correct color spillage by propagating color information from background to subject regions, allowing for efficient and high-quality cutout of subjects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If chroma keying is used to remove background, then background removal is achieved, but light spilling causes miscoloration of the subject
Solution Approach 1:
The image is segmented into three distinct regions: foreground (subject), background, and border region. This segmentation allows targeted processing of the border region where light spilling occurs, enabling separate correction of spill-affected areas while preserving the integrity of the main subject and background regions.
Solution Approach 2:
The correction method applies local quality by focusing computational resources specifically on the border region where light spilling is most problematic. The Laplacian matrix operations and recoloring are localized to this specific area rather than applying global corrections to the entire image, thereby efficiently addressing the miscoloration issue without unnecessary processing elsewhere.
2Manufacturing precision
If existing spill correction methods are applied, then some color correction is achieved, but satisfactory correction degree is not reached
Solution Approach 1:
The Laplacian matrix serves as an intermediary mathematical structure that captures spatial relationships and color gradients in the image. By using this intermediate representation, the method can propagate color information from the background region through the border region to estimate and correct light spilling effects, achieving high-quality correction through a systematic intermediate step rather than direct pixel manipulation.
Solution Approach 2:
The method changes parameters by constructing and manipulating the Laplacian matrix, which encodes spatial and color relationships in a transformed parameter space. This parameter transformation allows the system to solve the ill-posed color correction problem by working with derivative relationships and gradient information rather than raw pixel values, leading to more accurate spill correction.
3Measurement precision
If manual supervision is used for color correction, then correction accuracy is improved, but processing time increases
Solution Approach 1:
The system performs self-service by automatically identifying the border region, constructing the Laplacian matrix, and executing the recoloring correction process without requiring manual user input or supervision. The algorithm autonomously segments the image, identifies spill-affected areas, and applies corrections based on the mathematical relationships encoded in the Laplacian matrix, thereby achieving both high accuracy and computational efficiency.
Data Source
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AI summary
Disclosed is an apparatus comprising a processing device and a method for color correction of an image in a processing device, the method comprising: obtaining an image; determining a Laplacian matrix of the image; obtaining a first region of the image, the first region being indicative of a part of the image to be color corrected; obtaining a second region of the image; obtaining a first recoloring image based on the second region; determining a first corrected region of a first corrected image based on the Laplacian matrix and the first recoloring image; and obtaining and outputting a corrected image based on the first corrected region of the first corrected image.