基于CNN-SICE学习器的图像对比度增强

Pooja Patel
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引用次数: 0

摘要

产生具有良好的对比度,生动的色彩和丰富的细节的自然场景是数码摄影的基本目标。然而,由于光照条件差和成像设备的动态范围有限,所获得的图像往往曝光不足或曝光过度。因此,对比度增强是提高记录图像质量和使图像细节更清晰的重要步骤。在图像增强方面已经做了很多研究工作。本文研究了使用机器学习方法的不同技术和算法,并设计了基于块的CNN学习者用于对比度增强。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CNN-SICE Learner Based Image Contrast Enhancement
Producing the natural scene with good contrast, vivid color and rich details is an essential goal of digital photography. The acquired images, however, are often under-exposed or over-exposed because of poor lighting conditions and the limited dynamic range of imaging device. Contrast enhancement is thus an important step to improve the quality of recorded images and make the image details more visible. Many research work have been done for image enhancement. In this paper, different techniques and algorithms using machine learning approach are studied and Block based CNN Learner is designed for contrast enhancement.
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