Image Processing Method Using Multi-Frequency Denoising and Sharpening
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Solution Overview
Problem
Existing image processing technologies face issues with detail loss and excessive noise during image resolution enhancement, leading to a negative impact on image quality due to the addition of high-frequency noise.
Innovation Solution
A method that performs denoising processing prior to sharpening, using techniques like NLM or guided filters, and filters images on multiple frequency levels to separate and adjust frequency information components, thereby reducing noise and enhancing image resolution and detail.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image resolution enhancement is performed using existing technologies, then image resolution is improved, but noise increases and image quality deteriorates
Solution Approach 1:
The patent performs denoising processing on the image before sharpening processing. By removing noise in advance through denoising operations (such as non-local means denoising or guided filter denoising), the subsequent sharpening process works on a cleaner image, preventing noise amplification and achieving resolution enhancement without excessive noise contamination.
Solution Approach 2:
The patent divides the image processing into multiple frequency levels (at least two preset frequency levels). By separating the image into different frequency components and processing each level independently with appropriate filters, the system can enhance resolution while selectively controlling noise in different frequency bands, avoiding uniform noise amplification across all frequencies.
2Measurement precision
If sharpening processing is performed to enhance image resolution, then image detail is improved, but edge halation occurs
Solution Approach 1:
The patent performs denoising processing before sharpening to remove noise that would otherwise be amplified during edge enhancement. By cleaning the image in advance, the sharpening operation can focus on enhancing true image details without creating artificial edge halation artifacts from noise interference.
Solution Approach 2:
The patent applies different filtering operations to different frequency levels and regions. By using frequency-selective filtering and processing different frequency components with appropriate methods, the system enhances edges and details locally where needed while avoiding excessive enhancement that would cause halation in other regions.
3Measurement precision
If multi-frequency filtering is performed to enhance image resolution, then image quality is improved, but processing complexity increases
Solution Approach 1:
The patent divides the image into multiple frequency levels and processes each level with specific filters. This segmentation allows targeted processing of different frequency components, improving image quality by addressing specific frequency-related issues without needing to process the entire image uniformly, thus managing complexity through division.
Solution Approach 2:
The patent changes processing parameters (such as filter types, frequency levels, and processing intensity) based on the specific frequency level and image characteristics. By adaptively adjusting parameters for different frequency components, the system achieves high image quality while optimizing processing efficiency and managing computational complexity through parameter optimization.
Data Source
AI summary
An image is obtained. A denoised image is determined by performing denoising processing on the image based on a preset denoising method. At least two frequency information components of the denoised image are obtained by performing filter processing on a preset region image of the denoised image on at least two preset frequency levels. A sharpened image is obtained by performing superposition processing on the denoised image based on at least two frequency information components and a preset adjustment parameter. A target image is determined based on the sharpened image.