用于SAR图像亮度增强的超像素分割与分类

Harathi, Boyella Mala Konda Reddy
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引用次数: 0

摘要

总的来说,远距离探测照片是在雾、雪、薄云、泥泞等昏暗的条件下拍摄的,带来了画面对比的不幸。暗通道先验(Dark Channel Prior, DCP)用于消除对远距离检测图像的模糊影响。在该模型中,对于特征图像和远距离检测图像都可以实现初始化。提高卫星图像性能的第一步是确定图像是特征图像还是远距离探测图像,然后对其进行复原,以消除模糊。重点是利用航光值,其次是利用DCP来限制尘埃,最后是利用远距离探测图像(IDERS)模型的迭代除雾措施来消除雾。低光图像升级(LIME)措施的后效是无雾图像,清晰度扩大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Super pixel segmentation and classification of SAR images for brightness enhancement
By and large, distant detecting photos are taken in dim conditions like mist, snow, slim cloudiness, mud, etc, bringing about picture contrast misfortune. The Dark Channel Prior (DCP) was utilized to eliminate the dimness impact on far off detecting pictures in this examination. DE inception is conceivable in this model for both characteristic and distant detecting pictures. The initial phase in improving satellite picture properties is to decide if the picture is a characteristic picture or a far off detecting picture, and afterward recuperate it to take out dimness. Emphasis proceeds with the utilization of airlight values, trailed by the utilization of DCP to limit dust, lastly the fog is eliminated utilizing the Iterative dehazing measure for distant detecting picture (IDERS) model. The aftereffect of the Low light picture upgrade (LIME) measure is a fog free picture with expanded lucidity.
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