Depth Image Restoration via Sparse Representation Model
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Solution Overview
Problem
Existing guided depth image restoration methods face challenges in achieving high-precision restoration due to manual parameter setting, limited relationship modeling between depth and intensity images, and inconsistent coefficients during training and testing stages, particularly in handling complex local structures and high-dimensional data.
Innovation Solution
An information processing apparatus and method that acquires low-quality and high-quality depth images along with intensity images, performing a training process to derive parameters for an analysis sparse representation model, which models the relationship between these images, enabling accurate guided depth image restoration by outputting derived parameters for improved image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual parameter setting is used in existing guided depth image restoration methods, then the methods can be applied to different images, but the restoration precision is limited and cannot achieve high-precision restoration
Solution Approach 1:
The system performs self-learning by automatically deriving parameters through training processes using paired depth images and intensity images. The analysis sparse representation model learns optimal parameters from data without manual intervention, enabling the system to serve itself in parameter optimization while achieving high restoration precision.
Solution Approach 2:
The invention transforms fixed manual parameters into learnable parameters that are automatically optimized through training. The analysis sparse representation model adjusts parameters based on training data from paired depth and intensity images, dynamically changing parameters to achieve high restoration precision across different images without manual setting.
2Device complexity
If filtering-based methods use only first-order pixel relationships, then the methods are computationally simple, but they cannot effectively measure complex local structures
Solution Approach 1:
The invention transitions from first-order pixel relationships to higher-order relationships by incorporating intensity image information as an additional dimension. The analysis sparse representation model uses multi-dimensional features from both depth and intensity images to capture complex local structures, going beyond simple pixel-to-pixel relationships.
Solution Approach 2:
The method combines depth image data with intensity image data to create a composite representation. By fusing information from both image types through the analysis sparse representation model, the system achieves superior capability in measuring complex local structures compared to using depth images alone.
3Measurement precision
If dictionary-learning-based methods use high-dimensional feature vectors, then the statistical dependence between depth and intensity images can be modeled, but the calculation load becomes very big during training and testing stages
Solution Approach 1:
The invention extracts only the essential parameters needed for modeling the relationship between depth and intensity images, rather than using comprehensive high-dimensional feature vectors. The analysis sparse representation model identifies and uses key parameters from training data, reducing dimensionality while maintaining accurate statistical dependence modeling and significantly lowering calculation load.
Solution Approach 2:
The method transforms high-dimensional dictionary learning parameters into a more efficient parameter representation through the analysis sparse representation model. By changing the parameterization approach and learning optimal parameters from paired images, the system achieves accurate statistical dependence modeling with reduced computational complexity during both training and testing.
4Productivity
If data-driven training strategies use the same coefficient for both intensity and depth images, then the training can be performed, but inconsistent coefficients occur between training and testing stages
Solution Approach 1:
The invention applies different coefficients locally to depth images and intensity images during training, rather than using a single global coefficient. The analysis sparse representation model learns separate optimal coefficients for each image type from paired training data, ensuring that each modality is treated according to its specific characteristics. This local differentiation maintains coefficient consistency between training and testing stages while preserving training efficiency.
Data Source
AI summary
The disclosure relates to information processing apparatus and information processing method. The information processing apparatus according to an embodiment includes a processing circuitry configured to acquire a first depth image, a second depth image and an intensity image having a pixel correspondence with each other, wherein the second depth image being superior to the first depth image in terms of image quality. The processing circuitry is further configured to perform a training process based on the first depth image, the second depth image and the intensity image to derive parameters of an analysis sparse representation model modeling a relationship among the first depth image, the second depth image and the intensity image. The processing circuitry is configured to output the derived parameters.