Dynamic Texture Patch Training for Image Detail Restoration
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Solution Overview
Problem
Existing image processing technologies face challenges in restoring lost texture components, leading to blurred images and deteriorated details, especially when expanding low-resolution images to high-resolution formats like 4K or 8K UHD, due to image compression techniques such as MPEG/H.264/HEVC.
Innovation Solution
An image processing apparatus and method that utilizes a training network model to apply a texture patch to pixel blocks in an input image, where the texture patch is trained based on the image's characteristics, allowing for dynamic updating and classification to enhance image details and restore lost texture, using machine learning algorithms to identify and update texture patches based on similarity comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed texture patch is applied for restoring lost texture components, then the texture restoration process is simple and fast, but the image quality deteriorates and details are lost
Solution Approach 1:
The patent implements dynamic texture patch selection and updating mechanisms. The system dynamically adjusts texture patches based on image characteristics, pixel block analysis, and training feedback. Texture patches are not fixed but are continuously optimized through machine learning models that adapt to different image content, thereby improving image quality while maintaining processing efficiency.
Solution Approach 2:
The system changes parameters of texture patches through machine learning training processes. The training network model modifies texture patch characteristics based on learned patterns from training images, adjusting parameters such as texture frequency, orientation, and intensity to match the specific content being restored, thus improving restoration quality without sacrificing speed.
2Device complexity
If a texture patch less appropriate for an image is applied, then the processing complexity is reduced, but the image detail restoration is insufficient
Solution Approach 1:
The patent applies local quality principles by analyzing pixel blocks and selecting texture patches specific to each local region. Different parts of the image receive customized texture patches based on their local characteristics (edges, smooth regions, textures). This localized approach ensures appropriate texture restoration for each region without requiring complex global processing, balancing simplicity and effectiveness.
Solution Approach 2:
The system performs preliminary actions by pre-training texture patches using training network models before actual image restoration. The machine learning models are trained in advance on large datasets to learn effective texture patterns. This preliminary training reduces the complexity of real-time processing while ensuring high-quality restoration, as the heavy computational work is done beforehand.
3Loss of substance
If image compression techniques such as MPEG/H.264/HEVC are used, then the data transmission and storage efficiency is improved, but texture loss occurs and image quality deteriorates
Solution Approach 1:
The patent converts the harm caused by compression artifacts into a benefit by using them as training data for the machine learning model. The system learns to recognize and reconstruct compressed image patterns, transforming the degradation caused by MPEG/H.264/HEVC compression into an opportunity to develop specialized restoration techniques that are optimized for these specific compression artifacts, thereby recovering texture quality while maintaining compression efficiency.
Data Source
AI summary
An image processing apparatus and method are provided. The image processing apparatus includes: a memory configured to store at least one instruction, and a processor electrically connected to the memory, wherein the processor, by executing the at least one instruction, is configured to: apply an input image to a training network model; and apply, to a pixel block included in the input image, a texture patch corresponding to the pixel block to obtain an output image, wherein the training network model stores a plurality of texture patches corresponding to a plurality of classes classified based on a characteristic of an image, and is configured to train at least one texture patch, among the plurality of texture patches, based on the input image.