APPLICATION OF THE LASSO FUNCTION IN COMPRESSIVE SENSING FOR IMAGE RECONSTRUCTION OF MICROWAVE TOMOGRAPHY - Kurnia Imanda, Dian
Tomography is a technique for observing the inside of an object without having to damage the object. Microwave tomography is one of tomographic techniques using microwave frequencies with the advantage such as a relatively small antenna of used device. This study conducted a case study of the application of the least absolute shrinkage and selection operator (LASSO) function in the compressive sensing (CS) method for microwave tomography image reconstruction applications and analyzed its performance with image quality parameters. LASSO is a reconstruction algorithm in CS which is a linear model with ????1 regularization. The research consisted of image processing with test image and image processing with measurement data. Image processing with test image using a photo of a tree trunk object with different pixel sizes. Image processing from the measurement data uses the signal results captured by the receiving antenna after passing through a hollow wooden tree object. Performance was assessed using the mean square error (MSE), peak signal to noise ratio (PSNR), and structural similarity index measure (SSIM) parameters against the ???? value of the LASSO function in CS. In general, the image reconstruction results show the best quality values at the smallest ????. The reconstruction results for image processing with test image at 256 × 256 pixels have the best MSE value of 0.001, the best PSNR value of 78.130 dB, and the best SSIM value of 0.944 with the longest computation time of 2 hours 11 minutes 53 seconds. While the reconstruction results for signal processing with measurement data at a frequency of 2.885 GHz in a size of 24 × 24 pixels have the best MSE and SSIM with values of 0.1293 and 0.1838 with an average computation time of 30 seconds for one test.