CAJ | 학술논문

为了实现织物疵点图像的有效消噪，使其更有利于特征提取和疵点检测，提出了基于轮廓波变换的织物疵点图像消噪新方法。综合考虑轮廓波方向子带能量的大小与织物疵点图像轮廓细节之间的关系，对Donoho多尺度分解阈值进行修正，改进了Donoho多尺度分解阈值对图像细节“过扼杀”的缺点。实验结果表明，对织物疵点图像进行基于轮廓波变换改进阈值消噪时，该方法更好地保留了织物疵点图像的轮廓细节，峰值信噪比显著提高。采用改进的轮廓波Donoho多尺度分解阈值消噪后的图像，可以更好地应用于织物疵点图像的特征提取和疵点识别。
위료실현직물자점도상적유효소조，사기경유리우특정제취화자점검측，제출료기우륜곽파변환적직물자점도상소조신방법。종합고필륜곽파방향자대능량적대소여직물자점도상륜곽세절지간적관계，대Donoho다척도분해역치진행수정，개진료Donoho다척도분해역치대도상세절“과액살”적결점。실험결과표명，대직물자점도상진행기우륜곽파변환개진역치소조시，해방법경호지보류료직물자점도상적륜곽세절，봉치신조비현저제고。채용개진적륜곽파Donoho다척도분해역치소조후적도상，가이경호지응용우직물자점도상적특정제취화자점식별。
In order to achieve effective denoising of fabric defect images, making them more conducive to defect detection and feature extraction, a new fabric defect image denoising method based on contourlet transform is proposed. Considering the relationship between directional sub-bands energy of contourlet and the outline details of fabric defect images, the Donoho multi-scale decomposition threshold is corrected to improve its shortcoming that“over kill”to image detail. Experimental results show that the fabric defect images retain better outline details and the peak signal to noise ratio is improved significantly when contourlet transform denoising based on improved threshold is used. The fabric defect images which are denoised by improved Donoho multi-scale decomposition threshold can be better applied to feature extraction of fabric defect images and defect detection.