为了更好地实现图像的对比度增强,提出了一种基于图像检索的对比度增强模型。利用图像检索技术检索到与待增强图像内容相似的高质量图像作为参考图像指导图像增强。通过结合上下文相关、上下文不相关,以及亮度调整等增强方法,利用待增强图像及检索到的参考图像之间的美学特征、图像熵来指导求解多准则优化问题,并通过质量约束得到最终的增强图像。其中:上下文相关增强方法利用滤波器进行非锐化掩模,实现边缘增强;上下文不相关增强方法基于Sigmod函数进行映射变换,以符合人眼视觉感受;亮度调整增强方法先将图像进行自适应亮度均衡处理,然后进行对比度限制的自适应直方图均衡变换。实验结果表明,所提模型可以有效调整图像的对比度,与其他方法相比效果更好。

A contrast enhancement model based on the image retrieval is proposed to achieve the image contrast enhancement. We use the image retrieval technology to retrieve the high quality image as a reference image whose content is similar to the image to be enhanced to guide the enhancement of the image. The image entropy and aestheticis features between the image to be enhanced and the image retrieved are used to solve the multi-criteria optimization problems which combined the images via three enhancement methods such as the context-free, context-sensitive and brightness adjustment. Finally, the enhanced image is gotten by the quality constraint. In the context-sensitive enhancement, we use filter for unsharp masking to achieve edge enhancement. The context-free enhancement methods map transformations based on the Sigmod function to meet the human visual perception. The part of brightness adjustment firstly performs adaptive brightness equalization processing on the image, and then, a contrast limit adaptive histogram equalization transform is performed. Experimental results show that the proposed model can effectively adjust the contrast of the image and has better performance than other methods.