Abstract
In recent years, accurate Gaussian noise removal has attracted considerable attention for mobile applications, as in smart phones. Accurate conventional denoising methods have the potential ability to improve denoising performance with no additional time. Therefore, we propose a rapid post-processing method for Gaussian noise removal in this paper. Block matching and 3D filtering and weighted nuclear norm minimization are utilized to suppress noise. Although these nonlocal image denoising methods have quantitatively high performance, some fine image details are lacking due to the loss of high frequency information. To tackle this problem, an improvement to the pioneering RAISR approach (rapid and accurate image super-resolution), is applied to rapidly post-process the denoised image. It gives performance comparable to state-of-the-art super-resolution techniques at low computational cost, preserving important image structures well. Our modification is to reduce the hash classes for the patches extracted from the denoised image and the pixels from the ground truth to 18 filters by two improvements: geometric conversion and reduction of the strength classes. In addition, following RAISR, the census transform is exploited by blending the image processed by noise removal methods with the filtered one to achieve artifact-free results. Experimental results demonstrate that higher quality and more pleasant visual results can be achieved than by other methods, efficiently and with low memory requirements.
Highlights
Image denoising aims to efficiently recover an original image x from a noisy measurement y = x + n: y is the observed noisy image, x is the latent clean image and n is defined by additive white Gaussian noise with zero mean and variance σn2
We propose an accurate Gaussian noise removal method by applying improved RAISR (IRAISR) as a rapid post-processing step based on an extension of Rapid and accurate image super resolution (RAISR) [12]; it preserves image details in the denoised image because it can compensate for distorted high frequency information
weighted nuclear norm minimization (WNNM) post-processed by IRAISR efficiently outperforms the other competing methods for the Airplane, Butterfly, Cameraman, and Peppers images which are rich in edge regions; this is true for all noise levels because the image details in the edge regions can be well restored in our proposed method
Summary
In weighted nuclear norm minimization (WNNM) [7], the vectorized similar patches that are typically stacked by block matching are transformed into matrices and noise is suppressed using low-rank approximations. As these two methods search for patches at different locations similar to the reference patch, the performance is efficiently increased. We initially denoise the image using nonlocal denoising Both the patches extracted from the image by the nonlocal noise removal method and the pixels from the ground truth are classified into 18 hash classes with two improvements, including geometric conversion and reduction of gradient strength.
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