基于小波变换与深度残差融合的图像增强算法

Image enhancement algorithm based on wavelet transform fusion with deep residue

针对目前基于小波变换图像融合增强算法原始图像中的多尺度细节信息的不足,提出了一种改进的多尺度小波变换与深度残差选择相结合的图像增强算法。利用小波变换对原始图像进行分解提取得到它的多级分解系数后,再利用不同规则对不同层次的小波系数进行重构,与此同时引入深度残差算法的思想对子带系数做残差。对于高频子带系数,计算子带残差的系数与梯度特征融合方法的系数,选用两者最大值进行融合增强;而对于低频子带系数则采用梯度特征融合增强系数与子带残差系数取平均值的算法进行融合。通过在MATLAB平台上的实验对所提出算法进行验证,峰值信噪比相较于对比的方法都有所提高,且均方根误差也得到减小,结构相似度都得到提高,结果表明该算法能增强图像的多尺度细节信息,提高图像的信噪比,且具有更好的图像增强效果。

Aiming at the current lack of multi-scale detail information in the original image based on wavelet transform image fusion enhancement algorithm,an improved image enhancement algorithm combining multi-scale wavelet transform and depth residual selection is proposed.After the original image is decomposed and extracted by wavelet transform to obtain its multi-level decomposition coefficients,different rules are used to reconstruct different levels of wavelet coefficients.At the same time,the idea of deep residual algorithm is introduced to make residuals for subband coefficients.For the high frequency subband coefficients,the proposed algorithm will calculate the coefficients of the subband residuals and the coefficients of the gradient feature fusion method,and select the maximum value of the two for fusion enhancement,while for the low frequency subband coefficients,the algorithm uses the method of averaging the gradient feature fusion enhancement coefficient and the subband residual coefficient for fusion.The algorithm is verified through experiments on MATLAB platform,compared with the comparison method,the peak signal-to-noise ratio has been improved,and the root mean square error has also been reduced,and the structural similarity has been improved.The experimental results show that the method can enhance the multi-scale detail information of the image,improve the signal-to-noise ratio of the image,and has a better image enhancement effect.