An Adaptive Total Generalized Variation Model with Augmented Lagrangian Method for Image Denoising

<table>Evolutions of <svg height="13.55" id="M188" style="vertical-align:-0.20064pt" version="1.1" viewbox="0 0 10.625 13.55" width="10.625" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
<g transform="matrix(.017,-0,0,-.017,.625,12.725)"><path d="M529 97q-70 -109 -136 -109q-41 0 -56 94q-23 144 -37 284q-38 -88 -99 -202.5t-93 -156.5q-26 -8 -76 -19l-9 21q71 78 145.5 193t124.5 232q-5 84 -15 128q-12 55 -29.5 75.5t-42.5 20.5q-21 0 -45 -13l-8 24q16 17 46 30t55 13q43 0 70 -46.5t40 -169.5
q27 -249 51 -392q7 -46 23 -46q24 0 70 60z" id="x1D706"></path></g>
</svg>s (a) and PSNRs (b) achieved by TGV<sup>2</sup>ID-ADMM (TGV<sup>2</sup>) and TGV<sup>1</sup>ID-ADMM (TGV<sup>1</sup>).</table>

Mathematical Problems in Engineering

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Figure 2

Figure 2: An Adaptive Total Generalized Variation Model with Augmented Lagrangian Method for Image Denoising 