A Total Fractional-Order Variation Regularized Reconstruction Method for CT

<div>Quantitative analyses for the reconstructed images by the TV and the TFVk from projection data with noise when we choose different regularization parameters <span class="nowrap"><svg height="12.7178pt" id="M237" style="vertical-align:-3.42947pt" version="1.1" viewbox="-0.0498162 -9.28833 170.02 12.7178" width="170.02pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z"></path></g><g transform="matrix(.013,0,0,-0.013,4.498,0)"><path d="M538 96L524 119C492 88 454 63 446 63C439 63 434 70 440 101C463 223 491 341 518 448H508L433 422L401 276C355 192 240 56 188 56C163 56 154 89 163 133C184 233 207 338 235 443L230 448L152 424L58 17C40 -60 23 -143 23 -185C23 -241 42 -261 63 -261C92 -261 117 -241 125 -227L124 -221C109 -209 89 -165 89 -103C89 -55 96 -13 105 26H107C121 -3 134 -12 151 -12C172 -12 194 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(a) NMSE. (b) PSNR.</div>

Mathematical Problems in Engineering

fig4

Figure 4

Figure 4: A Total Fractional-Order Variation Regularized Reconstruction Method for CT 