[Retracted] The Analysis about Compressed Sensing Reconstruction Algorithm Based on Machine Learning Applied in Interference Multispectral Images

<table class="table-group" id="tab2"><tr><td><table class="table"><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr class="thead"><td class="align_left">Algorithm</td><td class="align_center">D-AMP</td><td class="align_center">ReconNet</td><td class="align_center">SCDA</td></tr><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr><td class="align_left">PSNR (dB)</td><td class="align_center">17.51</td><td class="align_center">18.04</td><td class="align_center">19.59</td></tr><tr class="table-tr"><td colspan="4"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Reconstruction accuracy comparison results of D-AMP, ReconNet, and SCDA algorithms (the measurement rate is 0.10).</div>

Advances in Multimedia

tab2

Table 2

Table 2: [Retracted] The Analysis about Compressed Sensing Reconstruction Algorithm Based on Machine Learning Applied in Interference Multispectral Images 

Table 2 | [Retracted] The Analysis about Compressed Sensing Reconstruction Algorithm Based on Machine Learning Applied in Interference Multispectral Images 