转化(遗传学)
人工智能
图像(数学)
直方图
像素
模式识别(心理学)
计算机科学
翻译(生物学)
功能(生物学)
几何变换
计算机视觉
数学
算法
生物化学
化学
进化生物学
生物
信使核糖核酸
基因
作者
Jaemin Park,An Gia Vien,M Cha,Thuy Thi Pham,Hanul Kim,Chul Lee
标识
DOI:10.1016/j.jvcir.2023.103863
摘要
Most deep learning-based image enhancement algorithms have been developed based on the image-to-image translation approach, in which enhancement processes are difficult to interpret. In this paper, we propose a novel interpretable image enhancement algorithm that estimates multiple transformation functions to describe complex color mapping. First, we develop a histogram-based multiple transformation function estimation network (HMTF-Net) to estimate multiple transformation functions by exploiting both the spatial and statistical information of the input images. Second, we estimate pixel-wise weight maps, which indicate the contribution of each transformation function at each pixel, based on the local structures of the input image and the transformed images obtained by each transformation function. Finally, we obtain the enhanced image as the weighted sum of the transformed images using the estimated weight maps. Extensive experiments confirm the effectiveness of the proposed approach and demonstrate that the proposed algorithm outperforms state-of-the-art image enhancement algorithms for different image enhancement tasks.
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