基于改进SCT的室内场景微光图像增强模型

Yuxiang Cheng, Chong Yang
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引用次数: 0

摘要

对于弱光增强模型SCT,弱光增强在室内环境下,会出现一些通常不是物体特征的纹理。这些虚假的纹理会产生误解。针对这个问题,提出了一种改进的SCT模型,称为sct++。改进后的模型增加了空间均匀损失函数、色彩常数损失函数和亮度平滑损失函数。通过LOL数据集对比SCT模型和SCT++模型,发现SCT++模型的收敛速度大于SCT模型,并且收敛速度有所提高。最后,比较sct++、SCT、dce++、GAN、LIME、LLVE和RUAS,并选择PSNR、SSIM、NIQE、LPIPS、MSE等图像评价方法。结果表明,sct++模型在PSNR、LPIPS和MSE方面优于SCT模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Low-light image enhancement model based on improved SCT in indoor scenes
For the low-light enhancement model SCT, lowlight enhancement in an indoor environment, there will be some textures that are usually not characteristics of the object. These fake textures can generate misunderstandings. An improved SCT model is proposed in response to this problem, called SCT++. The improved model adds the spatial uniform loss function, the color constant loss function, and the brightness smoothing loss function. Comparing the SCT model and the SCT++ model through the LOL dataset, it is found that the convergence rate of the SCT++ model is greater than that of the SCT model, and the convergence rate is improved. Finally, SCT++, SCT, DCE++, GAN, LIME, LLVE, and RUAS were compared, and PSNR, SSIM, NIQE, LPIPS, MSE, and other image evaluation methods were selected. It is concluded that the SCT++ model is better than the SCT model in terms of PSNR, LPIPS, and MSE.
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