Fourier Spectrum Detection of Grid Noise in RGB Images
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Solution Overview
Problem
Grid pattern noise, a type of image noise characterized by periodic, high-frequency, grid-like patterns, is introduced in high-resolution images captured by camera sensors, leading to image degradation and is challenging to eliminate due to hardware limitations and complex, costly data acquisition processes.
Innovation Solution
A method and system utilizing a trained artificial intelligence (AI) model to detect and eliminate grid pattern noise by analyzing the Fourier magnitude spectrum map of RGB image data, identifying periodic peaks, and extracting noise based on peak distances exceeding a threshold, followed by image processing to generate grid pattern-free images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution camera sensors are used to capture images, then image resolution is improved, but grid pattern noise is introduced leading to image degradation
Solution Approach 1:
The patent extracts grid pattern noise from the captured image by transforming the image into the frequency domain using Fourier transform, identifying periodic noise patterns through spectrum analysis, and selectively removing these patterns while preserving the underlying image content. This separation approach allows high-resolution imaging while eliminating the harmful grid artifacts.
Solution Approach 2:
The patent introduces an intermediary processing stage involving Fourier transform and spectrum map analysis between image capture and final output. This intermediary frequency domain representation serves as a mediator that reveals the periodic structure of grid noise, enabling targeted removal without directly modifying the spatial domain image and thus preserving image resolution while eliminating noise.
2Object-affected harmful factors
If traditional methods are used to eliminate grid pattern noise, then noise reduction is achieved, but the process becomes complex, costly, and time-consuming
Solution Approach 1:
The patent enables the image processing system to automatically detect and eliminate grid pattern noise through self-contained algorithms that analyze the frequency spectrum, identify periodic patterns, and remove noise without requiring external calibration data or complex acquisition processes. The system serves itself by using the image's own frequency characteristics to guide the denoising operation.
Solution Approach 2:
The patent changes the parameter space from spatial domain to frequency domain through Fourier transform, making the periodic grid noise visible as distinct spectral peaks. This parameter transformation simplifies the noise identification and removal process by converting a complex spatial pattern recognition problem into a simpler frequency thresholding operation based on detected peak distances.
3Measurement precision
If complex data acquisition processes are used to address grid pattern noise, then noise elimination accuracy is improved, but processing time and cost increase
Solution Approach 1:
The patent performs preliminary frequency domain transformation and spectrum map generation on the captured image, which reveals the periodic structure of grid noise before the actual removal process. This preliminary analysis identifies the characteristic peak patterns in the frequency spectrum, enabling rapid and accurate noise detection without requiring time-consuming iterative processing or external calibration data.
Solution Approach 2:
The patent replaces complex mechanical or hardware-based noise elimination approaches with computational signal processing methods. By using Fourier transform and spectral analysis algorithms, the system achieves accurate noise detection and removal through mathematical operations rather than physical interventions, significantly reducing processing time and cost while maintaining high precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Effectively removes grid pattern noise from high-resolution images without modifying camera hardware, improving image quality and simplifying the process through automated detection and elimination.
Implementation Method 1
determining a spectrum map of at least one color channel of the image
Data Source
AI summary
A method for detecting grid pattern noise in an image, the method comprising: obtaining the image; determining a spectrum map of at least one color channel of the image; detecting an occurrence of periodic peaks in the spectrum map; determining a distance between a set of adjacent peaks based on a location of the periodic peaks in the spectrum map; detecting a presence of grid pattern noise in the image based on the distance; and eliminating, based on the detecting of the presence of the grid pattern noise, the grid pattern noise from the image, resulting in a revised image.