Superresolution of Noisy Remotely Sensed Images Through Directional Representations
This work addresses image enhancement for remote sensing applications, but it is incremental as it builds on existing shearlet and SME methods.
The authors tackled the problem of single-image superresolution for noisy remotely sensed images by developing an algorithm based on the discrete shearlet transform and sparse mixing estimators, achieving competitive performance in terms of PSNR and SSIM.
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.