IEEE Trans Image Process. 2018 Jun;27(6):2650-2663. doi: 10.1109/TIP.2018.2809472.
Single-image super-resolution (SR) reconstruction via sparse representation has recently attracted broad interest. It is known that a low-resolution (LR) image is susceptible to noise or blur due to the degradation of the observed image, which would lead to a poor SR performance. In this paper, we propose a novel robust edge-preserving smoothing SR (REPS-SR) method in the framework of sparse representation. An EPS regularization term is designed based on gradient-domain-guided filtering to preserve image edges and reduce noise in the reconstructed image. Furthermore, a smoothing-aware factor adaptively determined by the estimation of the noise level of LR images without manual interference is presented to obtain an optimal balance between the data fidelity term and the proposed EPS regularization term. An iterative shrinkage algorithm is used to obtain the SR image results for LR images. The proposed adaptive smoothing-aware scheme makes our method robust to different levels of noise. Experimental results indicate that the proposed method can preserve image edges and reduce noise and outperforms the current state-of-the-art methods for noisy images.
基于稀疏表示的单幅图像超分辨率（SR）重建最近引起了广泛关注。已知由于观测图像的退化，低分辨率（LR）图像容易受到噪声或模糊的影响，这将导致 SR 性能不佳。在本文中，我们提出了一种新颖的基于稀疏表示的鲁棒边缘保持平滑 SR（REPS-SR）方法。基于梯度域引导滤波设计了一个 EPS 正则化项，以保持图像边缘并减少重建图像中的噪声。此外，还提出了一种平滑感知因子，该因子通过对无人工干扰的 LR 图像噪声水平的估计自适应确定，以在数据保真项和所提出的 EPS 正则化项之间取得最佳平衡。使用迭代收缩算法获得 LR 图像的 SR 图像结果。所提出的自适应平滑感知方案使我们的方法能够抵抗不同水平的噪声。实验结果表明，该方法能够保持图像边缘，降低噪声，并在噪声图像方面优于当前最先进的方法。
