Pixel-Wise Texture Filters for Reliable Image Super-Resolution
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Solution Overview
Problem
Existing image super-resolution methods, such as Super-Resolution Convolutional Neural Networks (SRCNN), Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN), and Very Deep networks for Super-Resolution (VDSR), struggle to reliably process images with varying texture features, particularly face images, leading to issues like fuzzy edges and high-frequency noise.
Innovation Solution
An image processing method that determines a unique filter for each pixel based on its texture features, using local texture images and weight values to perform super-resolution processing, thereby enhancing the reliability and efficiency of image enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single super-resolution neural network is used for all image types, then the processing is simple and fast, but the processing reliability is poor for different texture types
Solution Approach 1:
The patent segments the image into multiple local image blocks and processes each block independently with tailored filter parameters. This segmentation allows different processing strategies for different texture regions, improving reliability without requiring a completely separate system for each image type.
Solution Approach 2:
The patent applies local quality by determining different filter parameters for different local image blocks based on their texture characteristics. Each block receives processing optimized for its specific texture type (e.g., face, building, natural scenery), which improves processing reliability while maintaining a unified processing framework.
2Manufacturing precision
If iterative back-projection is used to recover high-frequency details, then high-frequency information is recovered, but fuzzy edges and high-frequency noise are introduced
Solution Approach 1:
The patent determines different filter parameters for different local image blocks based on their texture characteristics. For blocks with edges or high-contrast features, the filter parameters are adjusted to preserve sharpness and avoid fuzzy edges. For flat-texture regions, the parameters are tuned to suppress noise while recovering high-frequency details, thus resolving the contradiction between detail recovery and noise introduction.
Solution Approach 2:
The patent changes filter parameters dynamically based on local image characteristics. By analyzing texture features of each local block and adjusting filter parameters accordingly, the system can recover high-frequency details in appropriate regions while avoiding the introduction of fuzzy edges and noise in regions where they would be harmful.
3Manufacturing precision
If texture-structure constraints are applied to recover high-frequency information, then high-frequency details are improved, but the processing complexity increases
Solution Approach 1:
The patent divides the image into multiple local image blocks and applies texture-structure constraints independently to each block. This segmentation reduces the overall computational complexity compared to applying constraints to the entire image at once, while still achieving effective high-frequency information recovery in each local region.
Solution Approach 2:
The patent applies texture-structure constraints selectively to local image blocks that benefit from high-frequency recovery, rather than uniformly to the entire image. This partial application reduces processing complexity while maintaining the benefits of high-frequency detail recovery where most needed.
Data Source
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AI summary
This application provides an image processing method, apparatus, a device, and the like. In the method, a special image filter is generated, and super-resolution is performed on an image based on the image filter, thereby improving an image super-resolution effect. The image filter includes filter parameters corresponding to each pixel in an image that requires super-resolution processing, and pixels with different texture features correspond to different filter parameters. The image super-resolution method, apparatus, device, and the like may be applied to various scenarios such as a video, a game, and photographing, to improve an image effect in these scenarios, and enhance user experience.