摘要
内容相关的分块处理自适应图像对比度增强算法能够自适应地处理多种降质图像。本算法通过分块分析和处理的手段,更好地利用了图像局部信息和细节信息来对图像进行更加细致的处理;通过建立参数化的增强函数,自动调节增强函数参数,实现针对不同特性的图像生成与之相适合的增强函数曲线的功能;通过对图像子块的内容分析,提取出与增强函数相关的特征,并根据这些特征自动生成与之相适应的增强参数。使用上述方法,无需人工干预就能自适应地处理多种不同降质特性图像。实验结果表明,在无任何人工干预的情况下,本算法对过亮、过暗、逆光、雾霾污染甚至是混合多种性质的复杂图像的增强效果均较为理想,充分体现了算法的广泛适应性。
In this paper, a content-based adaptive contrast enhancement using sub-block processing was presented, which can flexibly process a wide range of image with various features. It can meticulously process image by analyzing local characteristics and operating in sub-block. We generated an enhancement function with adjustable parameters, which can be used to process various images flexibly through adjusting the parameters. Instead of adjusting these parameters manually, the proposed algorithm can obtain the reasonable enhancement parameters automatically through extracting the relevant characteristics from local content of image. In this way, various images can be adaptively processed without manual intervention. Experimental results demonstrate that the proposed method can flexibly enhance various images, such as underexposure, overexposure, back-lighted, misted, even mixed several characteristics above-and produce ideal enhanced images.
出处
《计算机科学》
CSCD
北大核心
2014年第10期110-112,138,共4页
Computer Science
基金
中国自然科学基金(61171165)资助
关键词
对比度增强
分块处理
自适应处理
内容相关
Contrast enhancement, Overlapped sub-block, Adaptive processing, Content-based
