Image Decomposition Using Temporal Illumination Extraction
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Solution Overview
Problem
Existing image processing methods struggle to accurately decompose images into illumination and reflectance components, especially under varying lighting conditions, leading to degradation in image quality and recognition performance.
Innovation Solution
A computer-implemented method using a neural network-based image decomposition model that includes an illumination extraction model and a reflectance extraction model, trained with loss functions considering temporal correlation and brightness changes, to estimate illumination and reflectance maps, allowing for improved image decomposition and white balancing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used to decompose images into illumination and reflectance components, then the processing speed is maintained, but the decomposition accuracy deteriorates under varying lighting conditions
Solution Approach 1:
The patent employs a recurrent neural network (RNN) architecture that dynamically adapts to varying lighting conditions by processing temporal sequences of images. The model learns temporal correlations and brightness changes across frames, enabling dynamic adjustment to different illumination scenarios rather than relying on static decomposition methods
Solution Approach 2:
The invention changes the parameters used for image decomposition by incorporating temporal correlation information and brightness change characteristics into the neural network training process. This allows the model to capture dynamic lighting variations and improve decomposition accuracy under varying conditions
2Measurement precision
If a neural network-based decomposition model is used to improve image decomposition accuracy, then the decomposition quality is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the image decomposition task into two distinct components: an illumination extraction model and a reflectance extraction model. This segmentation allows each model to specialize in extracting specific characteristics, improving overall accuracy while enabling modular computation that can be optimized independently
Solution Approach 2:
The invention performs preliminary extraction of temporal correlation information and brightness change characteristics from image sequences before feeding them to the decomposition models. This preprocessing step prepares the data in an optimized format, reducing the computational burden during the actual decomposition process
3Reliability
If temporal correlation information is used to train the decomposition model, then the accuracy under varying lighting is improved, but the training time increases
Solution Approach 1:
The patent extracts and utilizes temporal correlation information and brightness change characteristics from existing image sequences during the training process. By taking out these temporal features from the input data, the model learns to adapt to lighting variations more effectively, improving recognition performance under varying conditions
Data Source
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AI summary
A device with image processing includes: an image acquirer configured to acquire a plurality of images each having a different brightness; and one or more processors configured to extract an illumination map for an input image of the images and an illuminant color of the input image from the input image and temporal correlation information of the plurality of images, based on an illumination extraction model.