In this paper, we propose a new method for image denoising. The new method based on non-subsampled shearlet (NSST), non-local means (NLM) and hard threshold. The method splits a noised image into three parts: low frequency sub-band, band-pass sub-band, high frequency sub-band. NLM filter is used in low frequency sub-band and high frequency sub-band to remove noise after inverse NSST. The hard threshold is applied to inhibit the noise in the band-pass sub-band. Finally merge the images to get the denoised image. Experimental results on greyscale images indicate that the proposed approach is competitive with respect to peak signal to noise ratio and structural similarity index measure with several state-of-the-art algorithms especially at low noise levels.
Single image super-resolution(SR) reconstruction aims to recover the corresponding high resolution(HR) image through one low resolution(LR) image. SR reconstruction is an ill-posed problem, therefore, an effective image prior knowledge is meaningful to reconstruct the missing details in the LR image. In this paper, we propose a SR method by making use of the directional properties of image edges to construct local smoothing prior and non-local similarity prior. We utilize the directionlet that can effectively represent the image edge direction information to extract the directional feature information, after that, these directional information is used in the reconstruction framework based on TV and NLM to better protect the sharp edges of the image and improve the reliability of self-similar weight. The experimental results demonstrate that the proposed algorithm outperforms some of the current SR methods in terms of quantitatively and qualitatively.