Image Processing Control Signal Texture Region Inference
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Solution Overview
Problem
Existing image processing technologies fail to effectively express appropriate textures in each region of an object, leading to suboptimal image quality due to the difficulty in defining and controlling physical parameters for texture representation.
Innovation Solution
An image processing system that uses a control signal generation unit and an image generation unit to infer an output image with region-specific textures, based on an inference model learned from a trainee image with predetermined texture labels, allowing for the direct control of textures using deep neural networks (DNNs) and texture labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional model-based processing is used for texture control, then the processing can be performed using established methods, but the ability to accurately express and control textures is insufficient due to difficulty in defining physical parameters
Solution Approach 1:
The patent changes the parameter representation from conventional physical parameters to deep learning model parameters (weights and biases of DNN). By representing textures through learned parameters rather than predefined physical parameters, the system achieves accurate texture expression while avoiding the complexity of defining and measuring physical texture parameters.
Solution Approach 2:
The patent replaces conventional model-based texture processing with a data-driven deep learning approach. Instead of using mechanical or physical models to control textures, the system uses neural networks to learn and generate texture representations, substituting the mechanical parameter-based system with an intelligent learning-based system.
2Manufacturing precision
If the same texture processing is applied to all objects or entire regions, then the processing is simple and uniform, but the texture expression is not appropriate for each specific region
Solution Approach 1:
The patent segments the image into multiple regions and applies different texture processing to each region using region-specific control signals. The DNN generates separate control signals for different regions, allowing each region to have its texture independently optimized rather than applying a uniform processing approach to the entire image.
Solution Approach 2:
The patent implements local quality by generating region-specific control signals that tailor the texture processing to the characteristics of each local region. Instead of applying the same texture parameters globally, the system adjusts texture parameters locally for each region based on the learned relationships from training data.
3Manufacturing precision
If deep learning-based texture control is implemented with region-specific processing, then appropriate textures can be expressed in each region, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent performs preliminary action by pre-training the DNN model on large datasets of training images and their corresponding textures. This pre-learning phase captures the complex relationships between control signals and texture outcomes, so that during actual processing, the system can directly apply the learned model without needing to perform complex real-time optimization, thereby reducing online computational complexity.
Data Source
AI summary
The present technology relates to an image processing device, an image processing method, a learning device, a generation method, and a program for enabling generation of an image in which an appropriate texture is expressed in each region.An image processing device according to the present technology generates a control signal indicating the texture of each region in an output image as an inference result on the basis of an input image to be processed, inputs the input image to an inference model, and infers the output image in which each region has a texture indicated by the control signal, the inference model being obtained by performing learning based on a trainee image and a training image, the trainee image being generated by performing predetermined image processing on the training image, the texture of each region being expressed by a texture label in the training image. The present technology can be applied to various kinds of devices that handle images, such as TV sets, cameras, and smartphones.