Dynamic Image Denoising via Frequency Domain Block Analysis
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Solution Overview
Problem
Conventional image denoising methods fail to dynamically adjust denoising parameters according to image complexity, leading to unsatisfactory results and side effects like loss of details and artifacts.
Innovation Solution
An image denoising method that dynamically adjusts the size of search blocks and comparison blocks, and denoising strength by transforming comparison blocks to the frequency domain and calculating a concentration degree of frequency parameter to determine optimal block sizes and strength parameters based on image complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional neighborhood filter is used for image denoising, then the denoising process is simple and fast, but the reconstruction result is unsatisfactory and loses image details
Solution Approach 1:
The patent segments the image processing into multiple stages: frequency domain transformation of comparison blocks, concentration degree calculation, dynamic block size determination, and multi-scale denoising. This segmentation allows each stage to optimize for its specific function, achieving both speed and detail preservation.
Solution Approach 2:
The patent dynamically adjusts search block sizes and comparison block sizes based on the calculated concentration degree of frequency parameters. Different regions of the image receive different block sizes adapted to their local complexity, optimizing both processing efficiency and denoising quality for each region.
2Ease of manufacture
If fixed-size search block and comparison block are used, then the algorithm is simple to implement, but it cannot adapt to different image complexity regions
Solution Approach 1:
The patent changes the parameters of search block size and comparison block size based on the concentration degree of frequency parameters. This dynamic parameter adjustment allows the algorithm to adapt to different image regions while maintaining a relatively simple implementation framework.
Solution Approach 2:
The patent replaces the mechanical fixed-size block approach with a frequency-domain analysis system. By transforming comparison blocks to frequency domain and calculating concentration degrees, the system automatically determines appropriate block sizes without manual intervention, achieving adaptability through mathematical transformation rather than mechanical adjustment.
3Manufacturing precision
If frequency domain transformation is applied to comparison blocks, then image details are better preserved, but the computational complexity increases
Solution Approach 1:
The patent extracts only the essential frequency information from comparison blocks by transforming them to frequency domain and calculating concentration degrees. This extraction approach captures the necessary information for adaptive block sizing without processing all frequency components, reducing computational complexity while maintaining detail preservation benefits.
4Manufacturing precision
If dynamic adjustment of block sizes and strength parameters is implemented, then denoising performance is improved, but the processing time increases
Solution Approach 1:
The patent performs preliminary frequency domain transformation and concentration degree calculation to determine optimal block sizes and strength parameters before the actual denoising process. This preliminary analysis enables the subsequent denoising to proceed efficiently with pre-determined parameters, reducing overall processing time while maintaining high denoising quality.
Data Source
AI summary
An image denoising method according to the present invention includes the steps of: sequentially selecting a pixel in an image as a current pixel; dynamically determining a current search block and a strength parameter; transferring the comparison block of each pixel in the current search block to a frequency domain; determining a current frequency basis; obtaining a similarity between each neighborhood pixel and the current pixel in the current search block according to the current frequency basis; determining a weighting of each neighborhood pixel related to the current pixel according to the strength parameter, and a distance and the current pixel in the current search block; and weighted averaging each neighborhood pixel and the current pixel in the current search block according to the weighting so as to obtain a reconstruction value of the current pixel.