Key research themes
1. How can noise source predominance mapping improve strategic environmental noise maps for better public communication and control?
This research theme focuses on the development and application of noise source predominance maps (NSP) as an innovative visualization tool in environmental noise mapping. These maps enhance the standard strategic noise maps by identifying predominant noise sources spatially, enabling better understanding, communication, and targeted noise control strategies. The work addresses the limitations in current noise maps where average noise levels from combined sources are presented, obscuring distinct contributions, thus hampering public awareness and regulatory action.
2. What are effective methodological advances for detecting, characterizing, and reducing noise in digital images?
This theme covers noise modeling, detection, and denoising methods in digital image processing, highlighting the importance of appropriate noise characterization for efficient noise reduction. Research explores noise statistical models such as Gaussian, Salt and Pepper, Speckle, and Rayleigh noise, and innovative techniques including wavelet transform-based denoising, statistical distribution modeling, and advanced filtering algorithms that preserve image quality while attenuating noise.
3. How can advanced spatial data filtering and error assessment improve the reliability of noise-related environmental maps?
This theme investigates methods for improving environmental noise map accuracy by filtering outliers and quantifying noise impact resilience in spatially dense and noisy data sets. It addresses the challenge of systematic measurement noise, local data anomalies, and methodological robustness, emphasizing the importance of filtering approaches and noise-resilience horizons for enhancing the precision and interpretability of environmental maps.