Super Resolution Image Device Branch Circuit Segmentation
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Solution Overview
Problem
Existing super-resolution image generating devices using Generative Adversarial Networks (GAN) technologies face limitations in processing image blocks discriminatively, cannot cancel artifacts, and fail to differentiate between image blocks when generating high-resolution images.
Innovation Solution
A super-resolution image generating device comprising an image scaling-up circuit, front-end circuit, first branch circuit, second branch circuit, and arithmetic circuit, which extracts features, scales images, and combines pixel values to generate high-resolution images while allowing discriminative processing of image blocks and cancellation of artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the whole input image is processed with blended weights, then the image generation is simplified, but the ability to process different image blocks discriminatively is lost
Solution Approach 1:
The patent divides the input image into multiple image blocks and processes each block independently through separate branch circuits. The first branch circuit processes some image blocks while the second branch circuit processes other image blocks, enabling discriminative processing of different regions. This segmentation allows each branch to specialize in specific types of image processing for different blocks.
Solution Approach 2:
Different image blocks are processed with different models (first branch circuit and second branch circuit) according to their specific characteristics. This allows local optimization where each image block receives the appropriate processing model, improving the overall quality and adaptability of the super-resolution system.
2Manufacturing precision
If GAN technology is used to generate image details, then image details are improved, but artifacts are introduced
Solution Approach 1:
The patent segments the image processing into multiple branches where the second branch circuit uses GAN technology to generate image details while the first branch circuit processes other regions. This segmentation allows controlled application of GAN technology only where needed, minimizing artifact generation while maximizing detail improvement.
Solution Approach 2:
The patent introduces an intermediary mechanism that combines outputs from different branch circuits. The arithmetic circuit combines results from the first and second branch circuits, and the image scaling-up circuit integrates these results to produce the final output, mediating between the detail-enhancing GAN approach and the artifact-reducing first branch approach.
3Manufacturing precision
If model B is used to generate image details, then image details are improved, but the ability to cancel artifacts is reduced
Solution Approach 1:
The patent assigns different functions to different branch circuits: the first branch circuit is optimized for stable image generation with fewer artifacts, while the second branch circuit is optimized for generating image details. This segmentation allows the system to leverage the artifact-cancellation capability of the first branch while obtaining detailed output from the second branch.
Solution Approach 2:
The patent merges the outputs of the first and second branch circuits through the arithmetic circuit and image scaling-up circuit. This combination allows the system to integrate the artifact-reducing strengths of model A with the detail-enhancing strengths of model B, achieving both reliability and manufacturing precision in the final output.
Data Source
AI summary
A super resolution image generating device capable of processing an image flexibly includes a scaling-up circuit, a front-end circuit, a first branch circuit, a second branch circuit, and an arithmetic circuit. The scaling-up circuit scales up the image to generate an enlarged image including N pixel values. The front-end circuit extracts features of the image to generate a front-end feature map. The first branch circuit extracts features of the front-end feature map to generate a first feature map, and scales up the first feature map to generate N first values. The second branch circuit processes the front-end feature map to generate a second feature map, scales up the second feature map to generate N second values, and processes the N second values to generate N processed values. The arithmetic circuit combines the N pixel values, the N first values, and the N processed values to generate a super resolution image.