针对红外图像信噪比低, 易受背景环境影响的问题, 提出一种基于不可分离小波的多尺度方向分析(NSWMDA)和连接突触计算网络(LSCN)的图像增强算法. 该算法首先将原始图像进行冗余提升的不可分离小波变换(NSWT), 得到高频细节子带和低频近似子带, 然后对高频细节子带进行多方向滤波后LSCN算法进行增强, 对低频近似子带直接采用LSCN算法增强, 最后对处理后的子图进行融合重构得到增强后的红外图像. 在电力变压器红外图像中, 该算法相比其他算法在边缘强度、信息熵、峰值信噪比、结构相似度、平局梯度5种指标中分别至少提升了10.86%、14.39%、19.95%、7.06%、6.70%. 实验结果表明, 该算法不仅提升了红外图像整体清晰度, 同时也使得图像的细节纹理和对比度得到加强, 具有很好的红外图像增强效果.
To address the low signal-to-noise ratio of infrared images and their vulnerability to the impact of the background environment, this study proposes an image enhancement algorithm based on non-separable wavelet based multiscale directional analysis (NSWMDA) and linking synaptic computation network (LSCN). Firstly, the original image is subjected to non-separable wavelet transform (NSWT) with redundant lifting, which yields a high-frequency detail subband and a low-frequency approximation subband. Then, the high-frequency detail subband is filtered in multi-direction before its enhancement by the LSCN algorithm, while the low-frequency approximation subband is directly enhanced by the LSCN algorithm. Finally, the processed sub-images are fused and reconstructed to constitute the enhanced infrared image. In the infrared image of a power transformer, the edge strength, information entropy, peak signal-to-noise ratio, structural similarity and average gradient of the proposed algorithm are at least 10.86%, 14.39%, 19.95%, 7.06% and 6.70% higher than those of other algorithms. The experimental results show that the algorithm not only improves the overall clarity of the infrared image but also strengthens the detail texture and contrast of the image. It has a good infrared image enhancement effect and thus a bright application prospect for power equipment detection.