Med-SRNet: GAN-Based Medical Image Super-Resolution via High-Resolution Representation Learning

<div>Qualitative results on the constructed medical image database. 1–4 columns are the original images, bicubic interpolation (<span class="nowrap"><svg height="8.69875pt" id="M32" style="vertical-align:-0.3499298pt" version="1.1" viewbox="-0.0498162 -8.34882 14.0226 8.69875" width="14.0226pt" 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="M249 635C141 635 70 555 70 471C70 401 114 353 179 316C143 294 106 267 90 252C68 231 45 202 45 157C45 50 130 -12 237 -12C322 -12 435 52 435 169C435 256 372 304 303 343C349 374 375 398 383 407C401 429 411 458 411 487C411 569 344 635 249 635ZM238 603C285 603 337 567 337 482C337 422 310 385 276 358C205 393 145 426 145 500C145 552 179 603 238 603ZM248 20C183 20 125 70 125 163C125 218 158 268 206 300C284 261 355 217 355 143C355 66 308 20 248 20Z"></path></g><g transform="matrix(.013,0,0,-0.013,6.24,0)"><path d="M528 54L331 254L528 455L492 493L294 291L96 493L60 455L257 254L60 54L96 16L294 217L492 16L528 54Z"></path></g></svg>)</span> images, SR images by CGAN, and SR images by the proposed Med-SRNet.</div>

Computational Intelligence and Neuroscience

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Figure 4

Figure 4: Med-SRNet: GAN-Based Medical Image Super-Resolution via High-Resolution Representation Learning 