Remote Building Image De-noising via Spectrum Constraints
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Solution Overview
Problem
Conventional de-noising methods for remote images of ground buildings captured by high-speed aircraft are ineffective against strong sensor noise, often causing image blurring and loss of details, and are time-consuming and weak in real-time performance.
Innovation Solution
A de-noising method using spectrum constraints involves obtaining a reference image, performing Fourier transformations, threshold segmentation, erosion, and dilation to create a binary template, which is then used to filter real-time images in the frequency domain, followed by inverse Fourier transformation to generate a denoised image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional filtering methods are used for de-noising, then weak noise can be reduced, but strong sensor noise remains ineffective and image details are lost
Solution Approach 1:
The patent segments the frequency spectrum into distinct regions (low-frequency, mid-frequency, high-frequency) and applies different filtering strategies to each region. The spectrum is divided using frequency thresholds, allowing selective noise reduction in specific bands while preserving important image details in other bands, thereby resolving the contradiction between noise reduction and detail preservation.
Solution Approach 2:
The patent applies local quality by using adaptive filtering coefficients that vary across different frequency regions and spatial locations. The filtering strength is adjusted locally based on the noise characteristics and image content in each region, enabling effective noise reduction in homogeneous areas while preserving edges and details in complex regions.
2Reliability
If conventional filtering methods are used for de-noising, then noise reduction can be achieved, but the methods are time consuming and weak in real-time performance
Solution Approach 1:
The patent performs preliminary action by pre-calculating filtering coefficients and frequency threshold values based on statistical analysis of the image data. These pre-computed parameters are stored and reused during real-time processing, significantly reducing the computational burden during actual de-noising operations while maintaining high de-noising quality.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting filtering parameters based on local image characteristics such as noise variance and edge strength. The filtering coefficients and frequency thresholds are adapted to match the local content, enabling efficient processing with varying de-noising strength across different regions without requiring exhaustive computation.
3Reliability
If conventional filtering methods are used for de-noising, then processing can be performed, but the methods largely blur the image
Solution Approach 1:
The patent segments the frequency spectrum and applies selective filtering to different frequency bands. By preserving high-frequency components that contain edge and detail information while filtering noise in lower frequency bands, the method reduces noise without significantly blurring the image, thus resolving the contradiction between noise reduction and sharpness preservation.
Solution Approach 2:
The patent applies partial action by selectively applying filtering only to specific frequency regions and spatial locations where noise is predominant, rather than uniformly filtering the entire image. This selective approach reduces noise in homogeneous regions while leaving edges and detailed structures largely unaffected, maintaining image sharpness.
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
This method significantly improves the peak signal to noise ratio (PSNR) of denoised images, preserves edge and detail information, and achieves excellent real-time performance by effectively filtering noise while minimizing image blurring.
Implementation Method 1
performing a Fourier transformation on the reference image to obtain an amplitude spectrum
Data Source
AI summary
A de-noising method for remote images of ground buildings using spectrum constraints. The method includes: 1) obtaining a reference image of ground buildings from a remote image database of the ground buildings, performing a Fourier transformation on the reference image to obtain an amplitude spectrum, and performing a threshold segmentation, an erosion operation and a dilation operation successively on the amplitude spectrum to obtain a binary template of spectrum of the ground buildings; and 2) obtaining a real-time image of the ground buildings by a high-speed aircraft, performing a Fourier transformation on the real-time image to obtain a spectrum, filtering the spectrum of the real-time image in frequency domain by the binary template of spectrum of the ground buildings, and performing an inverse Fourier transformation thereon to generate a filtered real-time image of the ground buildings.