School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China.
Sensors (Basel). 2021 Dec 13;21(24):8330. doi: 10.3390/s21248330.
Since signal-dependent noise in a local weak texture region of a noisy image is approximated as additive noise, the corresponding noise parameters can be estimated from a given set of weakly textured image blocks. As a result, the meticulous selection of weakly textured image blocks plays a decisive role to estimate the noise parameters accurately. The existing methods consider the finite directions of the texture of image blocks or directly use the average value of an image block to select the weakly textured image block, which can result in errors. To overcome the drawbacks of the existing methods, this paper proposes a novel noise parameter estimation method using local binary cyclic jumping to aid in the selection of these weakly textured image blocks. The texture intensity of the image block is first defined by the cumulative average of the LBCJ information in the eight neighborhoods around the pixel, and, subsequently, the threshold is set for selecting weakly textured image blocks through texture intensity distribution of the image blocks and inverse binomial cumulative function. The experimental results reveal that the proposed method outperforms the existing alternative algorithms by 23% and 22% for the evaluative measures of MSE (a) and MSE (b), respectively.
由于噪声图像中局部弱纹理区域的信号相关噪声可近似为加性噪声，因此可以从给定的一组弱纹理图像块中估计相应的噪声参数。因此，精心选择弱纹理图像块对于准确估计噪声参数起着决定性的作用。现有的方法考虑图像块的纹理的有限方向或者直接使用图像块的平均值来选择弱纹理图像块，这可能会导致误差。为了克服现有方法的缺点，本文提出了一种使用局部二进制循环跳跃来辅助选择这些弱纹理图像块的新的噪声参数估计方法。首先通过像素周围的八个邻域中的 LBCJ 信息的累积平均值来定义图像块的纹理强度，然后通过图像块的纹理强度分布和逆二项式累积函数来设置选择弱纹理图像块的阈值。实验结果表明，所提出的方法在 MSE(a)和 MSE(b)的评价指标上分别比现有替代算法好 23%和 22%。
