Image Sharpening Using Edge-Preserving Filters
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Solution Overview
Problem
Current image sharpening techniques face challenges in distinguishing noise and texture components, leading to noisier images, as they equally affect all areas of an image, potentially losing edge information due to the reliance on simple low-pass filters.
Innovation Solution
The method employs a combination of unsharp masking, multiscale decomposition, and edge-preserving filtering, using different kernel sizes for low-pass and edge-preserving filters to selectively enhance detail regions while maintaining texture in the image, thereby improving image sharpening by discriminating smooth and detail regions effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple low-pass filtering is applied for sharpening, then processing complexity is reduced, but edge information is lost and noise cannot be distinguished from texture
Solution Approach 1:
The patent segments the image processing into multiple scales by applying low-pass filtering at different kernel sizes (first kernel size and second kernel size larger than third and fourth kernel sizes). This multi-scale decomposition allows the system to distinguish between noise and texture components while preserving edge information, resolving the contradiction between simple processing and information preservation.
Solution Approach 2:
The patent introduces the dimension of scale by using multiple kernel sizes for filtering. By processing the image at different scales and combining the results through subtraction and addition operations, the system can selectively enhance details while preserving edges, moving beyond a single-scale approach.
2Ease of operation
If uniform filtering is applied across the entire image, then processing is simplified, but noise and texture components cannot be discriminated
Solution Approach 1:
The patent applies local quality by using edge-preserving filtering that adapts to local image characteristics. The filter automatically adjusts its behavior based on whether a region contains edges or smooth areas, allowing precise discrimination between noise and texture components while maintaining operational simplicity through automated local adaptation.
3Manufacturing precision
If aggressive sharpening is applied to enhance details, then image sharpness is improved, but noise is also amplified
Solution Approach 1:
The patent converts the harmful effect of noise amplification into a benefit by using the difference between multi-scale filtered images. The subtraction operation isolates high-frequency components (details and noise), which are then selectively enhanced while the edge-preserving filtering特性 allows the system to distinguish between actual texture details and noise, enhancing sharpness while controlling noise amplification.
Data Source
AI summary
A method and an apparatus are provided for sharpening an image, by an image processor of an electronic device. An input image is received. Low pass filtering is applied to the input image to generate a first image and a second image. A kernel size of first image and the second image are different. Edge preserving filtering is applied to the input image to generate a third image and a fourth image. A kernel size of the third image and the fourth image are different. The first image is subtracted from the third image to obtain a first resulting image. The first image has a larger kernel size than the third image. The second image from the fourth image to obtain a second resulting image. The second image has a larger kernel size than the fourth image. The first resultant image, the second resultant image, and the input image are summed to generate a sharpened image.