Image Denoising Algorithm Based on Gradient Domain Guided Filtering and NSST

Abstract

Traditional image denoising methods, which do not depend on data training, have good interpretability. However, traditional image denoising methods hardly achieve the denoising effect of deep learning methods. Based on traditional image processing techniques, this paper proposes a new hybrid image denoising model. The block-batching and 3-D filtering (BM3D) algorithm is used to obtain the first denoised image. The weighted kernel norm minimization (WNNM) and non-subsampled shearlet transform (NSST) algorithms are successively adopted to get the second denoised image. By the gradient domain guided filtering, the texture information of the first denoised image is extracted to enhance the details of the second denoised image. Specially, we propose the adaptive iterative NSST algorithm based on the improved soft thresholding, in order to solve the problems about the discontinuity of the hard thresholding and the constant deviation of the soft thresholding. Our approach can not only attenuate excessive smoothing, but also restore the natural appearance of the image. Experiments are conducted to demonstrate that our proposed method enjoys PSNR and SSIM performance gains over several deep learning denoising methods.