Abstract
We develop an algorithm for single-image superresolution of remotely sensed data, based on the discrete shearlet transform. The shearlet transform extracts directional features of signals, and is known to provide near-optimally sparse representations for a broad class of images. This often leads to superior performance in edge detection and image representation when compared to isotropic frames. We justify the use of shearlets mathematically, before presenting a denoising single-image superresolution algorithm that combines the shearlet transform with sparse mixing estimators (SME). Our algorithm is compared with a variety of single-image superresolution methods, including wavelet SME superresolution. Our numerical results demonstrate competitive performance in terms of PSNR and SSIM.
Abstract (translated)
我们基于离散剪切变换开发了一种用于遥感数据单图像超分辨率的算法。剪切变换提取信号的方向特征，并且已知为广泛类别的图像提供近似最优稀疏的表示。与各向同性帧相比，这通常导致边缘检测和图像表示的优越性能。在提出将剪切变换与稀疏混合估计（SME）相结合的去噪单图超分辨率算法之前，我们证明了数学上使用剪切的合理性。我们的算法与各种单图像超分辨率方法进行了比较，包括小波SME超分辨率。我们的数值结果表明在PSNR和SSIM方面具有竞争力。
URL
https://arxiv.org/abs/1602.08575
