Analysis of Reconstructed Images Using Compressive Sensing
نویسنده
چکیده
Traditionally image reconstruction is done by performing Fast Fourier Transform (FFT). But recently there has been growing interest in using compressive sensing (CS) to perform image reconstruction.In compressive sensing, the main property of signal-Sparsity is explored for reconstruction purposes.In this paper, for image reconstruction, various optimization techniques like L1 optimization, Total Variation (TV) Minimization and Split-Bregman Optimization is used. Among these, the Split-Bregman reconstruction algorithm shows good performances. This is proved by the analysis of reconstructed images using the quality measures such as Peak Signal-to-Noise Ratio (PSNR) and Normalised Absolute Error (NAE). Index Terms – FFT, Compressive Sensing, L1, TV, Split-Bregman, Quality Measures —————————— ——————————
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