CAJ | 학술논문

为了更好地实现3维激光扫描图像的去噪，提出一种融合直方图结构相似度（ HSSIM）和残差比阈值的改进稀疏去噪算法。利用初始化过完备字典进行稀疏分解，以相似因子代替重构误差作为保真项，利用残差比阈值作为正交匹配追踪算法的迭代终止条件对图像进行去噪，获得了去噪后图像的峰值信噪比及HSSIM指标。结果表明，与基于db2小波变换、多尺度曲波变换和离散余弦变换的去噪方法相比，该算法能获得更好的峰值信噪比和HSSIM数据。在有效去除图像噪声的同时还能更有效地保留图像的细节特征。
위료경호지실현3유격광소묘도상적거조，제출일충융합직방도결구상사도（ HSSIM）화잔차비역치적개진희소거조산법。이용초시화과완비자전진행희소분해，이상사인자대체중구오차작위보진항，이용잔차비역치작위정교필배추종산법적질대종지조건대도상진행거조，획득료거조후도상적봉치신조비급HSSIM지표。결과표명，여기우db2소파변환、다척도곡파변환화리산여현변환적거조방법상비，해산법능획득경호적봉치신조비화HSSIM수거。재유효거제도상조성적동시환능경유효지보류도상적세절특정。
In order to get better results of 3-D laser scanning image denoising , an improved sparse representation denoising algorithm was proposed by combining histogram structural similarity ( HSSIM ) and residual ratio threshold .The initial over-complete dictionary was applied in the sparse decomposition .The reconstruction error was replaced by similarity factor as fidelity factor.Then the residual ratio threshold was used as the iteration termination of the orthogonal matching pursuit algorithm to reconstruct the denoised image .Finally, the performance data of denoised image , such as peak signal-to-noise ratio(PSNR) and HSSIM, were obtained.The experimental results show that the proposed method could provide better PSNR and HSSIM results compared with the image denoising methods using db 2 wavelet transform , multiscale curve wave transform and discrete cosine transform.Meanwhile , the structural features can be reserved effectively by the proposed method .