Abstract
Image denoising is a fundamental task in computer vision and image processing system with an aim of estimating the original image by eliminating the noise and artefact from the noise-corrupted version of the image. In this study, a nonlocal means (NLM) algorithm with NSST (non-subsampled shearlet transform) has been designed to surface a computationally simple image denoising algorithm. Initially, NSST is employed to decompose source image into coarser and finer layers. The number of decomposition levels of NSST is set to two, resulting in set of low-frequency coefficients (coarser layer) and four sets high-frequency coefficients (finer layers). The two number of levels of decomposition are used in order to preserve memory, reduce processing time, and mitigate the influence of noise and misregistration errors. The finer layers are then processed using NLM algorithm, while the coarser layer is left as it is. The NL-Means algorithm reduces noise in finer layers while maintaining the sharpness of strong edges, such as the image silhouette. When compared to noisy images, this filter preserves textured regions, resulting in retaining more information. To obtain a final denoised image, inverse NSST is performed to the coarser layer and the NL-means filtered finer layers. The robustness of our method has been tested on the different multisensor and medical image dataset with diverse levels of noise. In the context of both subjective assessment and objective measurement, our method outperforms numerous other existing denoising algorithms notably in terms of retaining fine image structures. It is also clearly exhibited that the proposed method is computationally more effective as compared to other prevailing algorithms.
Highlights
Image denoising is the process to eliminate noise or distortions from the images
(A) Experimental setup With the aim to test the effectiveness of our denoising method, we have utilized three images: house image, magnetic resonance imaging (MRI) image and panchromatic (PAN) image that is remote sensing, medical and natural image, respectively [55]
An effectiveness of our method is compared with nonlocal version of generalised RTV (NLGRTV) [30], Locally Adaptive Kernel Regression (LARK) [31], Total variation minimization [32], Bitonic filter [33], GBFMT [34], NLFMT [35], RBF [36], Markov Random Field (MRF) [37] and SBF [38]
Summary
Image denoising is the process to eliminate noise or distortions from the images. It is mostly used as image preor post-processing to boost the quality of the processed images for further image analysis and understanding. Regardless their ability to improve denoising quality, this patch level NSS prior utilized in this method undergo one major obstacle i.e. they tend to introduce artefacts around the edges This can be attributed to the fact that finding nearly identical patches for complete reference patches in the natural image is quite difficult, when the number of identical patches is more. High-contrast elements such as textures in general are rarely preserved while suppressing noise As a result, this serves as a motivation to use a combination of non-subsampled Shearlet transform and a nonlocal means algorithm to denoise images. The advantages of combined adaptable approach especially exhibit the capacity to extract multidimensional data geometry It could effectively indicate edges in high-noise images.
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