Color Image Denoising Based on Guided Filter and Adaptive Wavelet Threshold

<table class="figure-group"><tr class="fig-image" id="a"><td><object data="https://static.hindawi.com/articles/acisc/volume-2017/5835020/figures/5835020.fig.009a.svgz" name="5835020.fig.009a" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(a) </b><svg height="11.7978pt" id="M142" style="vertical-align:-0.2063999pt" version="1.1" viewbox="-0.0498162 -11.5914 49.1072 11.7978" width="49.1072pt" 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(.0091,0,0,-0.0091,7.347,-5.741)"><path d="M414 144C384 79 371 75 317 75H135L276 221C367 316 408 376 408 465C408 570 327 635 237 635C179 635 131 609 100 575L42 494L67 471C94 510 138 565 205 565C277 565 321 517 321 435C321 348 258 270 195 195C146 137 88 81 33 26V0H411C423 44 433 88 446 135L414 144Z" id="g50-51"></path></g><g transform="matrix(.013,0,0,-0.013,15.926,0)"><path d="M535 323V373H52V323H535ZM535 138V188H52V138H535Z" id="g117-34"></path></g><g transform="matrix(.013,0,0,-0.013,27.188,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z" id="g113-49"></path></g><g transform="matrix(.013,0,0,-0.013,33.428,0)"><path d="M113 -12C146 -12 170 11 170 46C170 78 146 103 114 103S58 78 58 46C58 11 82 -12 113 -12Z" id="g113-47"></path></g><g transform="matrix(.013,0,0,-0.013,36.392,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z" id="g113-49"></path></g><g transform="matrix(.013,0,0,-0.013,42.632,0)"><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></svg></td></tr><tr class="fig-image" id="b"><td><object data="https://static.hindawi.com/articles/acisc/volume-2017/5835020/figures/5835020.fig.009b.svgz" name="5835020.fig.009b" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(b) </b>Bilateral filtering</td></tr><tr class="fig-image" id="c"><td><object data="https://static.hindawi.com/articles/acisc/volume-2017/5835020/figures/5835020.fig.009c.svgz" name="5835020.fig.009c" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(c) </b>Adaptive wavelet</td></tr><tr class="fig-image" id="d"><td><object data="https://static.hindawi.com/articles/acisc/volume-2017/5835020/figures/5835020.fig.009d.svgz" name="5835020.fig.009d" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(d) </b>Nonlocal means</td></tr><tr class="fig-image" id="e"><td><object data="https://static.hindawi.com/articles/acisc/volume-2017/5835020/figures/5835020.fig.009e.svgz" name="5835020.fig.009e" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(e) </b>BM3D</td></tr><tr class="fig-image" id="f"><td><object data="https://static.hindawi.com/articles/acisc/volume-2017/5835020/figures/5835020.fig.009f.svgz" name="5835020.fig.009f" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(f) </b>Proposed method</td></tr></table>

<div>Image 9: comparison of various experimental results under noise variance <span class="nowrap"><svg height="11.7978pt" id="M141" style="vertical-align:-0.2063999pt" version="1.1" viewbox="-0.0498162 -11.5914 49.1072 11.7978" width="49.1072pt" 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(.0091,0,0,-0.0091,7.347,-5.741)"><path d="M414 144C384 79 371 75 317 75H135L276 221C367 316 408 376 408 465C408 570 327 635 237 635C179 635 131 609 100 575L42 494L67 471C94 510 138 565 205 565C277 565 321 517 321 435C321 348 258 270 195 195C146 137 88 81 33 26V0H411C423 44 433 88 446 135L414 144Z" id="g50-51"></path></g><g transform="matrix(.013,0,0,-0.013,15.926,0)"><path d="M535 323V373H52V323H535ZM535 138V188H52V138H535Z" id="g117-34"></path></g><g transform="matrix(.013,0,0,-0.013,27.188,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z" id="g113-49"></path></g><g transform="matrix(.013,0,0,-0.013,33.428,0)"><path d="M113 -12C146 -12 170 11 170 46C170 78 146 103 114 103S58 78 58 46C58 11 82 -12 113 -12Z" id="g113-47"></path></g><g transform="matrix(.013,0,0,-0.013,36.392,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z" id="g113-49"></path></g><g transform="matrix(.013,0,0,-0.013,42.632,0)"><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></svg>.</span></div>

Applied Computational Intelligence and Soft Computing

fig9

Figure 9

Figure 9: Color Image Denoising Based on Guided Filter and Adaptive Wavelet Threshold 