Exploring temporal dynamics of ice melting through dynamic laser speckle imaging

Abstract Dynamic laser speckle imaging (DLSI) is an emerging non-destructive imaging technique used for characterizing rough surfaces. High spatio-temporal resolution and cost effective nature make this imaging technique more attractive in optical metrology. In this study, DLSI is utilized to monitor the intermittent dynamics of ice during its melting process. In DLSI, image processing algorithms such as Time History of Speckle Patterns and Co-Occurrence Matrices are generally employed to process the speckle images. However, these algorithms are limited to gather information from specific regions of the speckle images. To overcome this limitation, frequency domain algorithms are employed to analyse the activity across all frequency bands. In this work, we utilized Wavelet-Based Entropy for the analysis of speckle images captured from the surface of ice during its melting process. Wavelet-Based Entropy is a powerful frequency domain tool for exploring dynamic processes, offering superior capability in capturing spatial and spectral variations, making it particularly well-suited for the analysis of speckle images. The results were also compared with Probability density function and Co-Occurrence Matrices and demonstrated consistent agreement.