摘要：

针对反光金属表面缺陷检测时遇到的缺陷信息难以捕获和处理的问题, 提出一种基于多尺度变换和图像增强的偏振图像融合方法。借助偏振成像技术抑制反光, 利用偏振相机采集到包含偏振信息的图像。从子块划分和平滑映射表两方面改进限制对比度自适应直方图均衡化(CLAHE)算法, 显著增强偏振度、偏振角及可见光图像的对比度。采用拉普拉斯金字塔(LP)分解图像, 对划痕、凹坑等所在的高频层图像进行双边滤波和拉普拉斯锐化, 增强高频细节。在图像融合阶段, 提出基于亮度自适应权重的融合策略, 根据各图像的亮度分布特征动态调整融合权重, 从而确保融合后的图像不会因亮度差异而模糊缺陷特征。重构融合后的图像金字塔, 得到融合图像。以洗衣机前封门为实验对象进行实验, 结果表明, 与其他图像融合方法相比, 所提方法在信息熵(IE)、峰值信噪比(PSNR)、平均梯度(AG)等客观评价指标上评估性能更高, 尤其在PSNR、结构相似性指数(SSIM)上取得了最高的65.304 3 dB、0.472 7。融合后图像具有较高的信噪比(SNR)和对比度, 能够突出反光金属表面的缺陷特征。

关键词: 图像融合, 偏振图像, 多尺度变换, 图像增强, 反光金属

Abstract:

This study proposes a polarization image fusion method based on multi-scale transformation and image enhancement to address the difficulty in capturing and processing defect information on reflective metal surfaces. Polarization imaging technology is used to suppress reflections, and images containing polarization information are captured using a polarization camera. The Constrained Contrast Adaptive Histogram Equalization (CLAHE) algorithm is improved by means of sub-block division and a smooth mapping table. These improvements significantly enhance the degree of polarization, polarization angle, and contrast of the visible images. A Laplacian Pyramid (LP) decomposition is applied to the images. Bilateral filtering and Laplacian sharpening are performed on the high-frequency layers, where defects such as scratches and pits are located, to enhance high-frequency details. In the image fusion stage, a fusion strategy based on adaptive luminance weights is proposed. The fusion weights are adjusted dynamically according to the luminance distribution of each image, ensuring that the defects are not blurred owing to luminance differences. The fused image pyramid is then reconstructed to obtain the final fused image. Experiments are conducted on the front sealed doors of washing machines. The results show that, compared with other image fusion methods, the proposed method achieves better performance in objective evaluation metrics such as Information Entropy (IE), Peak Signal-to-Noise Ratio (PSNR), and Average Gradient (AG). In particular, the PSNR and Structural Similarity Index (SSIM) reach the maximum values of 65.304 3 dB and 0.472 7, respectively. The fused image exhibits a high Signal-to-Noise Ratio (SNR) and contrast, effectively highlighting the defects on the reflective metal surfaces.

Key words: image fusion, polarization image, multi-scale transform, image enhancement, reflective metal