Innovative Infrared Image Denoising: Enhancing Detail Preservation through Optimized Pulse Coupled Neural Network Parameters
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Abstract
To address the issue of image blurring and the loss of edge information, as well as the common challenges of ambiguous targets and low contrast in infrared image preprocessing algorithms, this paper proposes a method for denoising infrared images using a simplified pulse-coupled neural network. The improved selection of neuron coupling strength, based on the gray value of neighboring pixels, enhances the denoising process. Furthermore, the computation of the threshold decaying exponent is simplified, making it dependent on the threshold amplitude, which can now be automatically optimized. Experimental results indicate that this method not only improves the efficiency of parameter optimization for the pulse-coupled neural network but also effectively filters out noise while preserving image details to the greatest extent possible.
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