Image Denoising Based on Adaptive Fractional Order with Improved PM Model

<table class="figure-group"><tr class="fig-image" id="a"><td><object data="https://static.hindawi.com/articles/mpe/volume-2018/9620754/figures/9620754.fig.003a.svgz" name="9620754.fig.003a" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(a) </b>Original</td></tr><tr class="fig-image" id="b"><td><object data="https://static.hindawi.com/articles/mpe/volume-2018/9620754/figures/9620754.fig.003b.svgz" name="9620754.fig.003b" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(b) </b>Noise (<svg height="8.55521pt" id="M123" style="vertical-align:-0.2063904pt" version="1.1" viewbox="-0.0498162 -8.34882 34.9223 8.55521" width="34.9223pt" 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="M548 455L523 469C505 448 490 439 461 439C416 439 374 447 316 447C144 447 23 310 23 161C23 47 89 -12 179 -12C312 -12 429 106 429 244C429 303 419 344 368 386L371 388C404 383 440 378 474 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data="https://static.hindawi.com/articles/mpe/volume-2018/9620754/figures/9620754.fig.003c.svgz" name="9620754.fig.003c" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(c) </b>Denoised (<svg height="8.55521pt" id="M124" style="vertical-align:-0.2063904pt" version="1.1" viewbox="-0.0498162 -8.34882 34.9223 8.55521" width="34.9223pt" 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="M548 455L523 469C505 448 490 439 461 439C416 439 374 447 316 447C144 447 23 310 23 161C23 47 89 -12 179 -12C312 -12 429 106 429 244C429 303 419 344 368 386L371 388C404 383 440 378 474 378C507 378 530 411 548 455ZM350 274C350 191 308 25 198 25C144 25 108 75 108 157C108 264 170 395 276 395C296 395 320 384 333 360C347 334 350 316 350 274Z" id="g113-240"></path></g><g transform="matrix(.013,0,0,-0.013,10.979,0)"><path d="M535 323V373H52V323H535ZM535 138V188H52V138H535Z" id="g117-34"></path></g><g transform="matrix(.013,0,0,-0.013,22.242,0)"><path d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z" id="g113-50"></path></g><g transform="matrix(.013,0,0,-0.013,28.482,0)"><path d="M153 550H386L412 615L406 623H120L82 318C104 327 142 338 184 338C294 338 347 275 347 187C347 112 305 39 221 39C160 39 119 71 97 89C88 97 80 96 71 90C59 80 50 67 49 57C48 45 52 36 66 23C80 9 123 -12 169 -12C221 -11 288 15 342 59C403 109 431 165 431 225C431 308 366 395 238 395C212 395 165 379 127 364L153 550Z" id="g113-54"></path></g></svg>)</td></tr><tr class="fig-image" id="d"><td><object data="https://static.hindawi.com/articles/mpe/volume-2018/9620754/figures/9620754.fig.003d.svgz" name="9620754.fig.003d" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(d) </b>Noise (<svg height="8.55521pt" id="M125" style="vertical-align:-0.2063904pt" version="1.1" viewbox="-0.0498162 -8.34882 34.9223 8.55521" 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<div>Denoising images with formula (<a href="https://static.hindawi.com/articles/mpe/volume-2018/9620754/figures/#EEq6">6</a>) as the diffusion coefficient.</div>

Mathematical Problems in Engineering

fig3

Figure 3

Figure 3: Image Denoising Based on Adaptive Fractional Order with Improved PM Model 