<ul><li>This paper focuses on developing new and fast algorithms for recovering a sparse vector from a small number of measurements in compressive sensing (CS).</li><li>Conventional methods like L1 minimization do not work well in coherent systems where measurements are correlated.</li><li>The L1-L2 norm difference, which has shown superior performance, is computationally expensive.</li><li>The paper presents an analytical solution for the proximal operator of the L1-L2 metric, making fast L1 solvers applicable and efficient.</li></ul>

Understanding L1/L2 minimization technique part3(Machine Learning 2024)

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