Akindele Romoke Grace, Yu Ming, Kanda Paul Shekonya, Owoola Eunice Oluwabunmi, Aribilola Ifeoluwapo
School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China.
School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China.
Sensors (Basel). 2023 Sep 10;23(18):7780. doi: 10.3390/s23187780.
The recovery of semantics from corrupted images is a significant challenge in image processing. Noise can obscure features, interfere with accurate analysis, and bias results. To address this issue, the Regularized Neighborhood Pixel Similarity Wavelet algorithm (PixSimWave) was developed for denoising Nifti (magnetic resonance imaging (MRI)). The PixSimWave algorithm uses regularized pixel similarity detection to improve the accuracy of noise reduction by creating patches to analyze the intensity of pixels and locate matching pixels, as well as adaptive neighborhood filtering to estimate noisy pixel values by allocating each pixel a weight based on its similarity. The wavelet transform breaks down the image into scales and orientations, allowing a sparse image representation to allocate a soft threshold on its similarity to the original pixels. The proposed method was evaluated on simulated and raw T1w MRIs, outperforming other methods in terms of an SSIM value of 0.9908 for a low Rician noise level of 3% and 0.9881 for a high noise level of 17%. The addition of Gaussian noise improved PSNR and SSIM, with the results indicating that the proposed method outperformed other models while preserving edges and textures. In summary, the PixSimWave algorithm is a viable noise-elimination approach that employs both sparse wavelet coefficients and regularized similarity with decreased computation time, improving the accuracy of noise reduction in images.
从受损图像中恢复语义是图像处理中的一项重大挑战。噪声会模糊特征、干扰准确分析并使结果产生偏差。为解决此问题，开发了正则化邻域像素相似性小波算法（PixSimWave）用于对Nifti（磁共振成像（MRI））进行去噪。PixSimWave算法使用正则化像素相似性检测，通过创建补丁来分析像素强度并定位匹配像素，从而提高降噪精度，同时使用自适应邻域滤波，根据每个像素的相似性为其分配权重来估计噪声像素值。小波变换将图像分解为尺度和方向，允许稀疏图像表示根据其与原始像素的相似性分配软阈值。该方法在模拟和原始T1w MRI上进行了评估，对于3%的低莱斯噪声水平，SSIM值为0.9908，对于17%的高噪声水平，SSIM值为0.9881，优于其他方法。添加高斯噪声提高了PSNR和SSIM，结果表明该方法在保留边缘和纹理的同时优于其他模型。总之，PixSimWave算法是一种可行的噪声消除方法，它采用稀疏小波系数和正则化相似性，减少了计算时间，提高了图像降噪的准确性。
