Image Texture Restoration via Selective CNN Processing
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Solution Overview
Problem
Current image processing technologies face challenges in efficiently restoring lost texture information and enhancing fragile texture details in images, particularly in regions with weak or missing textures, leading to poor image quality and authenticity issues.
Innovation Solution
An image optimization method that extracts texture quality information using a first convolutional neural network (CNN) and performs texture restoration on regions with low quality using a second CNN, while also enhancing texture features through a residual network with dilated convolutional layers, effectively utilizing both internal and external image information for improved authenticity and naturalness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If texture restoration is performed on regions with low quality using CNN-based methods, then image quality and authenticity are improved, but processing time and computational complexity increase
Solution Approach 1:
The image is divided into multiple regions based on texture quality assessment, with only low-quality regions undergoing restoration processing. This selective approach maintains high restoration quality while reducing overall processing time by excluding already-acceptable regions from intensive computation.
Solution Approach 2:
A texture quality assessment map is generated before restoration to identify regions requiring processing. This preliminary classification enables the system to pre-determine which areas need restoration, avoiding wasted computational resources on high-quality regions and streamlining the overall processing pipeline.
2Manufacturing precision
If texture feature enhancement is performed through residual networks with dilated convolutional layers, then texture detail information is enriched, but device complexity and computational requirements increase
Solution Approach 1:
Different processing intensities are applied to different regions: texture restoration is performed only on low-quality regions identified by the assessment map, while enhancement is applied selectively. This local differentiation reduces overall computational complexity while maintaining high texture detail quality where needed.
Solution Approach 2:
The system applies partial enhancement action by focusing computational resources only on regions that benefit most from texture enhancement, rather than processing the entire image uniformly. This selective approach reduces device complexity while achieving sufficient texture detail enrichment in critical areas.
3Manufacturing precision
If both texture restoration and enhancement are performed on the entire image, then comprehensive quality improvement is achieved, but processing efficiency and productivity decrease
Solution Approach 1:
The image processing pipeline is segmented into distinct stages: texture quality assessment, selective restoration on low-quality regions, and targeted enhancement. This segmentation allows the system to maintain comprehensive quality improvement while improving processing efficiency by avoiding redundant operations on already-acceptable regions.
Solution Approach 2:
The texture quality assessment map is generated as a preliminary step to guide subsequent restoration and enhancement operations. This preliminary classification enables the system to efficiently allocate computational resources, maintaining high overall image quality while significantly improving processing productivity through selective application of intensive operations.
Data Source
AI summary
The disclosure provides an image optimization method, system, and storage medium. The image optimization method includes extracting texture quality information from an input image. The texture quality information indicates a spatial distribution of texture quality in the input image. The image optimization method also includes performing, according to the texture quality information, texture restoration on a set region in the input image to generate a texture restored image.