基于伽玛矫正的图像细节特征增强算法仿真

Simulation of Image Detail Feature Enhancement Algorithm Based on Gamma Correction

图像很容易因光照不均匀导致质量下降,无法从图像中挖掘出有效信息,限制图像使用范围。通过单一增强方法只能将图像部分噪声滤掉,且易丢失细节特征,为此提出一种融合直方图均衡和双边滤波的图像增强算法。通过Key函数、归一化法将图像划分成三个子图,即高、中、低亮度直方图均衡图,采用积分算法、概率密度函数求解各子图像素点密度值,为了避免出现晕光或者过度增强的情况,采用伽玛矫正得出各子图像素最佳调节因子,按照此值调节子图像素灰度等级。利用双边滤波、权重函数弱化图像噪声、强化细节特征,最终得到较为清晰图像。实验结果表明,所提融合方法处理下图像像素点部分均匀、平均梯度值较大,图像增强效果良好,且细节保留良好。

Images are prone to quality degradation due to uneven lighting,so it is difficult to extract effective information from them.Moreover,the scope of image usage will also be limited.In addition,the single enhancement method can only filter out some noise from the image and is prone to losing detail features.To address this,an image enhancement algorithm based on histogram equalization and bilateral filtering was proposed.Firstly,the Key function and normalization method were used to divide the image into three sub-images,namely high,medium,and low brightness histogram equalization images.Then,the integral algorithm and probability density function were employed to solve the density value of each sub-image pixel point.In order to avoid halation or excessive enhancement,gamma correction was used to obtain the optimal adjustment factor of the sub-image pixel.According to this value,the subimage pixel gray level was adjusted.Furthermore,bilateral filtering and weight functions were utilized to weaken image noise and enhance detail features.Finally,a clearer image was obtained.Experimental results show that under the proposed method,the image pixels are partially uniform,and the average gradient value is large.At the same time,the image enhancement effect is good,and the details are well preserved.