An effective nonlocal means image denoising framework based on non-subsampled shearlet transform

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 artifact 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. There are three steps in our process; First, NSST is employed to decompose source image into coarser and finer layers. The number of decomposition level of NSST is set to two, resulting in one low frequency coefficient (coarser layer) and four high frequency coefficients (finer layers). The two levels of decomposition are used in order to preserve memory, reduce processing time, and reduce 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 also smoothes 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.

A Robust Medical Image Fusion Based on Synthetic Focusing Degree Criterion and Special Kernel Set for Clinical Diagnosis

“…All methods are implemented in MATLAB 2022b. For comparisons, we use seven algorithms, namely Laplacian pyramid and CNN reconstruction with local gradient energy strategy (LPCNNR) (Fu et al, 2020), an unsupervised Enhanced Medical image fusion network (EM Fusion) (Xu and Ma, 2021), MRPAN (Fu et al, 2021), a non-subsampled contourlet transform and CNN (NSCT-CNN) (Wang et al, 2021a), a Cross Encoder Fusion (CEFusion) (Zhu et al, 2022), FDGNet (Zhang et al, 2023b) and Joint Sparse Model with Coupled Dictionary (JSM-CD) (Zhang et al, 2023a) along with five metrics namely, Average Pixel Intensity (API) or mean (F ̅ ), Entropy (H), Average Gradient (AG), overall fusion efficiency (QAB/F) and information loss during fusion process (L AB/F ) (Goyal et al, 2023) to evaluate techniques subjectively and objectively.…”

The algorithm for extracting surface defects from ZrO2 ceramic bearing balls using shearlet transform image enhancement

“…Shearlet transform is a multi-scale and multi-direction geometric analysis method, 18,19 which is widely used in the field of image and signal processing. 20 It is a multi-scale and multidirectional signal-based analysis.…”

Innovative Dual-Stage Blind Noise Reduction in Real-World Images Using Multi-Scale Convolutions and Dual Attention Mechanisms

“…Accordingly, the image is processed using the shearlet transform, followed by the application of the Yaroslavsky's filter, which is weighted based on pixel similarities from the previously denoised image. Goyal et al [28] presented a computationally efficient algorithm that is based on non-local means combined with a non-subsampled shearlet transform (NSST). The source image is first decomposed using an NSST into coarser and finer layers.…”