With the increasing complexity of electromagnetic environments due to the surge of electronic devices, achieving reliable signal acquisition has become challenging. The wideband microwave imaging system (WMIS) serves as a promising solution to identify sources of electromagnetic interference (EMS) efficiently. This system utilizes advanced signal processing techniques to quickly localize interference sources, operating effectively within the frequency range of 1GHz to 6GHz.
Recent advancements have brought forth the development of the wavelet deep unfolded network for iterative stripe noise removal (WDUNINR) which significantly improves the image processing capabilities of wideband microwave imaging systems. Stripe noise, the unwanted linear interference appearing in acquired images, poses considerable challenges for effective source localization. The emergence of such noise is primarily attributed to inconsistencies within multi-channel signal acquisition systems, rendering many traditional denoising approaches inadequate.
The WDUNINR introduces innovative methodologies to address this pressing issue. At the core of this approach is the application of wavelet transforms, which allows for decomposing electromagnetic images to exploit the intrinsic characteristics of stripe noise. The proposed network utilizes Haar discrete wavelet transform to break down images, focusing on low-frequency components where stripe noise intensity is more concentrated.
One of the key enhancements offered by WDUNINR is its adoption of bidirectional gated recurrent units (BiGRUs) supplemented with spatial attention mechanisms. These components allow the model to leverage correlations between adjacent rows of data effectively, which is fundamental when striving to isolate and reduce stripe noise across images. This iterative method intelligently feeds the output of one processing step back as input for subsequent iterations, refining the noise estimation process dynamically.
Extensive validations conducted using simulation and experimental datasets reveal the robustness of this new methodology. Results exhibited notable improvements compared to classical de-striping methods, with qualitative assessments demonstrating clearer electromagnetic distributions and significant retention of original signal details. Specifically, the algorithm was shown to mitigate stripe noise effectively, resulting in enhanced localization accuracy of EMS.
Through rigorous testing, the researchers observed stripe noise intensity fluctuations ranging from 0.02 to 0.22 across different frequencies, with lower frequencies typically exhibiting higher levels of noise. By applying the WDUNINR, substantial reductions were achieved, marking it as superior to prior techniques. The method effectively bridges the performance gap left by existing solutions, making it a viable candidate for widespread integration within microwave imaging systems.
Future work will focus on optimizing computational efficiency to reap the full benefits of this innovative approach. Nonetheless, for current applications, WDUNINR marks a significant leap forward, promising not only enhanced accuracy in electromagnetic imaging but also paving the way for improved performance across diverse technological sectors requiring precise electromagnetic source localization.