研究目的
To improve infrared image quality by reducing noise through an adaptive threshold denoising method based on wavelet transform, specifically addressing multiplicative noise and Gibbs visual distortion.
研究成果
The proposed method effectively denoises infrared images by combining adaptive thresholding with binary wavelet transform, reducing noise and improving image quality while minimizing Gibbs distortion. It outperforms traditional wavelet threshold methods in terms of standard deviation and SNR improvements.
研究不足
The method assumes additive noise is negligible compared to multiplicative noise, which may not hold in all scenarios. The study uses specific wavelet functions and image sizes, potentially limiting generalizability. Computational complexity of the binary wavelet transform and adaptive thresholding might be high for real-time applications.
1:Experimental Design and Method Selection:
The study uses a wavelet-based denoising approach, transforming multiplicative noise to additive noise via logarithmic transformation, applying binary wavelet transform with translation invariance, and using an adaptive threshold function for denoising.
2:Sample Selection and Data Sources:
Infrared images of 182x208 pixels captured by a medical infrared camera from Chongqing Weilian Technology Limited are used.
3:List of Experimental Equipment and Materials:
A medical infrared camera (model not specified) from Chongqing Weilian Technology Limited; sym4 wavelet functions for processing.
4:Experimental Procedures and Operational Workflow:
Steps include logarithmic transformation of the image, binary wavelet decomposition using à trous algorithm, threshold quantization with the adaptive function, reconstruction, and exponential restoration.
5:Data Analysis Methods:
Performance is evaluated using metrics such as standard deviation, signal-to-noise ratio (SNR), F/MSE ratio, and normalized mean square error (NMSE).
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