本文在BayesShrink 閾值的基礎(chǔ)上，提出了一種計(jì)算更有效的自適應(yīng)的閾值估計(jì)，并
針對(duì)傳統(tǒng)的軟、硬閾值函數(shù)在去噪中的不足，提出一種新的去噪閾值函數(shù)，最后將所提出的圖象去噪方法與傳統(tǒng)方法進(jìn)行了實(shí)驗(yàn)比較，實(shí)驗(yàn)結(jié)果表明該方法在圖象去噪中取得較好的效果，有效地提高了信號(hào)去噪所得信噪比增益。
關(guān)鍵詞：正交小波變換、圖象去噪、閾值
中圖分類(lèi)號(hào):TN911.7 文獻(xiàn)標(biāo)識(shí)碼:A
An Improved Algorithms of Image Denosing Based on Orthogonal Wavelet Transform
LEI Hui ( School of Electrical and Infomational Engineering, Changsha University of Science & Technology,Changsha,410076) Abstract：This paper proposes an efficient threshold estimation method for image denoising based on BayesShrink. So the traditional methods for noise reduction using wavelet soft threshold function and hard threshold function have some defects, an improved threshold function is proposed, At the end, experiments have been done
between this new method and the traditional methods. The experiment results indicate
that this new method achieves better effect on reducing noise and gets higher SNR
gain for the reconstructed signal.
Keywords: orthogonal wavelet transform, Image denoising, threshold