Abstract: Multispectral laser speckle analysis techniques have been widely used for applications such as the analysis of seed qualities, detection of fungal infections in fresh produce, estimation of blood flow velocities, and the like. However, the conventional methods fail to address color bias in images which is a major concern in terms of accuracy in applications like activity prediction. and the present disclosure provides a novel image processing algorithm which performs activity prediction without any color bias. Here, an RGB input image is segmented, and mask is generated. Distance based clustering is performed on the masked image to obtain cluster map. Simultaneously, color coded activity map is generated for each color component of the image. Further, the color coded activity map is multiplied with cluster contrast values and thereby an optimal color coded activity region is selected for activity map generation.

Abstract: This disclosure relates to portable speckle imaging system and method for automated speckle activity map based dynamic speckle analysis. The embodiments of present disclosure herein address unresolved problem of capturing variations in speckle patterns where noise is completely removed and dependency on intensity of variations in speckle patterns is eliminated. The method of the present disclosure provides a correlation methodology for analyzing laser speckle images for applications such as seed viability, fungus detection, surface roughness analysis, and/or the like by capturing temporal variation from frame to frame and ignoring the intensity of speckle data after denoising, thereby providing an effective mechanism to study speckle time series data. The system and method of the present disclosure performs well in terms of time efficiency and visual cues and requires minimal human intervention.