圖9 RAISR在2x上採樣率時與SRCNN, A+等超分辨算法的技術指標對比
左圖為PSNR-runtime指標,右圖為SSIM-runtime指標【4】
結語
超分辨重建在醫學影像處理、壓縮圖像增強等方面具有廣闊的應用前景,近年來一直是深度學習研究的熱門領域。卷積和殘差構件的改進、不同種類perceptual loss的進一步分析、對抗生成網絡用於超分辨重建的探索等都是值得關注的方向。
參考文獻
[1] Dong, Chao, et al. "Image Super-Resolution Using Deep Convolutional Networks." IEEE Transactions on Pattern Analysis & Machine Intelligence 38.2(2016):295-307.
[2] Shi, Wenzhe, et al. "Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network." (2016):1874-1883.
[3] Johnson, Justin, A. Alahi, and F. F. Li. "Perceptual Losses for Real-Time Style Transfer and Super-Resolution." (2016):694-711.
[4] Romano, Yaniv, J. Isidoro, and P. Milanfar. "RAISR: Rapid and Accurate Image Super Resolution." IEEE Transactions on Computational Imaging 3.1(2016):110-125.
[5] Conkey, Donald B., et al. "Super-resolution photoacoustic imaging through a scattering wall." Nature Communications 6(2015):7902.