CAJ | 학술논문

针对矿物浮选过程中泡沫图像易受噪声影响，存在纹理细节模糊、灰度值对比度低等问题，提出一种浮选泡沫图像的非线性降噪方法。首先构造一种改进方向波变换，保证信号的平移不变性，同时采用提升算法减小其运算量。然后通过对分解系数建模，针对低频子带系数采用多尺度 Retinex 算法进行处理，以改善整体亮度均匀性，提高对比度；对各高通子带构建基于高斯混合尺度模型的分解系数邻域模型，并利用Bayes最小均方(BLS)估计进行局部去噪。最后利用所提出的方法对大量浮选泡沫图像进行去噪分析。结果表明：所提出的降噪方法能突出泡沫图像的纹理细节信息，提高泡沫图像的对比度，在信噪比和实时性上有明显提高，为后续泡沫图像的分割和工况识别奠定基础。
침대광물부선과정중포말도상역수조성영향，존재문리세절모호、회도치대비도저등문제，제출일충부선포말도상적비선성강조방법。수선구조일충개진방향파변환，보증신호적평이불변성，동시채용제승산법감소기운산량。연후통과대분해계수건모，침대저빈자대계수채용다척도 Retinex 산법진행처리，이개선정체량도균균성，제고대비도；대각고통자대구건기우고사혼합척도모형적분해계수린역모형，병이용Bayes최소균방(BLS)고계진행국부거조。최후이용소제출적방법대대량부선포말도상진행거조분석。결과표명：소제출적강조방법능돌출포말도상적문리세절신식，제고포말도상적대비도，재신조비화실시성상유명현제고，위후속포말도상적분할화공황식별전정기출。
Considering the defects, such as easy sensitivity to noise and heavy texture, low contrast of gray value in the process of the floatation of foam image, a non-linear de-noising method was proposed. Lifting improved directionlet transform was firstly constructed, which not only ensured the shifting invariance but reduced its complexity. Multi-scale Retinex algorithm dealing with low-frequency subband coefficient was proposed for improving luminance uniformity and overall contrast. For high-pass subband, a model of decomposition coefficients neighbourhood based on Gaussian scale mixtures model was proposed for de-noising the image locally using Bayes least square (BLS). The analysis on the effect of de-noising was given to lots of real froth images. The results show that the proposed method is successful in maintaining edges and is superior in de-noising in term of PSNR and visual effect. It lays a foundation for foamy segmentation and analyzing grade from flotation froth image.