Image Reconstruction and Evaluation: Applications on Micro-Surfaces and Lenna Image Representation

This article develops algorithms for the characterization and the visualization of microscale features by using a small number of sample points, and with a goal to mitigate for the measurement shortcomings which are often destructive or time consuming. The popular measurement techniques which are used in imaging micro-surfaces may include 3D stylus or interferometric profilometry and Scanning Electron Microscopy (SEM), where both generate can represent the surface characteristics in terms of 3D dimensional topology and greyscale image, respectively. Such images could be highly dense; therefore, traditional image processing techniques might be computationally expensive. We implement the algorithms in several case studies to rapidly examine the microscopic features of a Microelectromechanical System (MEMS) microsurface, and then validate the results on a famous greyscale image; i.e. “Lenna” image. The contribution of this research include first, we develop local and global algorithm based on modified Thin Plate Spline (TPS) model to reconstruct high resolution images of the micro-surface’s topography, and its derivatives by using low resolution images. Second, we obtain a bending energy algorithm from our modified TPS model, and use it to filter out image defects. Finally, we develop a computationally efficient Windowing technique, which combines TPS and Linear Sequential Estimation (LSE), to enhance the visualization of images. The Windowing technique allows rapid image reconstruction based on the reduction of inverse problem.