发表机构
National Institute of Technology Karnataka（卡纳塔克国家理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于无限方向性下半框架的盲图像去噪方法，通过协方差白化和下尾矩估计噪声，在标准图像上实现平均PSNR提升7.45 dB，有效抑制噪声并保持结构。
AI 中文摘要
针对加性高斯白噪声，提出了一种基于无限方向性下半框架（DLSF）的盲图像去噪框架。该模型采用尺度相关的方向性分析，并对无界半框架算子进行预解式正则化。通过在DLSF域中对四个方向差分通道的联合协方差进行建模，并应用协方差白化以获得卡方统计量，直接估计噪声方差。一种下尾矩估计器提供了无需中位绝对偏差的盲噪声估计。将估计的噪声水平纳入通道维纳型收缩和典型对偶合成中，随后进行具有自动停止的数据一致性迭代重建。在噪声水平15至30的三幅标准灰度图像上的实验，平均相对噪声估计误差为3.28%，PSNR平均提高7.45 dB，SSIM平均提高0.367。在30/255噪声下，估计误差降至1.73%，平均PSNR增益为8.31 dB。结果表明，该方法能有效抑制噪声并保持结构，在平滑和边缘主导图像上表现最佳。
英文摘要
A blind image denoising framework based on an infinite directional lower semi-frame (DLSF) is proposed for additive white Gaussian noise. The model employs scale-dependent directional analysis with resolvent regularization of the unbounded semi-frame operator. Noise variance is estimated directly in the DLSF domain by modeling the joint covariance of four directional difference channels and applying covariance whitening to obtain a chi-square statistic. A lower-tail moment estimator provides blind noise estimation without median absolute deviation. The estimated noise level is incorporated into channel-wise Wiener-type shrinkage and canonical-dual synthesis, followed by a data-consistent iterative reconstruction with automatic stopping. Experiments on three standard grayscale images at noise levels 15--30 yield a mean relative noise-estimation error of 3.28\%, with average improvements of 7.45 dB in PSNR and 0.367 in SSIM. At 30/255 noise, the estimation error decreases to 1.73\%, with a mean PSNR gain of 8.31 dB. Results demonstrate effective noise suppression and structural preservation, with the strongest performance on smooth and edge-dominated images.
