Noise variance estimation based on image segmentation
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摘要: 提出一種基于圖像分割的噪聲方差兩步估計算法.第一步,對含有噪聲的圖像進行平滑,再利用統計區域歸并算法對圖像進行分割,并計算每個區域的方差,根據統計規律選擇適當的區域估計圖像中噪聲方差.第二步,利用初始估計的方差,修正平滑濾波、圖像分割及噪聲估計的參數,進行新一輪的平滑、分割和方差估計,得出更為準確的估計結果.在大量圖像和不同噪聲情況下的實驗結果表明,該算法可以快速、準確地估計圖像中噪聲方差.Abstract: A new two-step noise variance estimation algorithm was proposed based on image segmentation. In the first step, a noisy image was smoothed and was segmented by the statistical region merge (SRM) algorithm, then the variance of each region was computed, and some regions were selected based on the statistical rule to estimate the noise variance. In the second step, the parame-ters of filtering, segmentation and estimation were revised according to the estimated noise variance, and a new cycle of image filte-ring, segmentation and estimation was performed to obtain more accurate estimation. Experimental results on large numbers of images and various noises show that the proposed algorithm can estimate the noise variance quickly and accurately.
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Key words:
- noise /
- variance analysis /
- estimation algorithms /
- image segmentation
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