计算机科学 ›› 2023, Vol. 50 ›› Issue (6A): 220700016-8.doi: 10.11896/jsjkx.220700016
梁美彦1, 张宇1, 梁建安1, 陈庆辉1, 王茹1, 王琳2,3
LIANG Meiyan1, ZHANG Yu1, LIANG Jianan1, CHEN Qinghui1, WANG Ru1, WANG Lin2,3
摘要: 高分辨率的病理学图像是疾病高精度诊断的客观依据,在精准医学领域具有重要意义。然而,受硬件设备分辨率和扫描时长的限制,实时获取高分辨率病理图像存在困难。经典的图像超分辨率重建算法由于模型的参数较难估计,导致重建后图像细节模糊且不够真实,不适用于病理学图像。为此,文中提出稀疏编码非局部注意力对偶网络,通过上采样和降采样对偶分支中的稀疏编码非局部注意力机制、高斯约束以及参数共享策略来实现病理学图像的超分辨率重建。重建后的病理图像峰值信噪比和结构相似性分别达到了30.84dB和0.914。研究结果表明,所提方法不但能够实现病理学图像中高频细节的精确重建,轻量化的稀疏编码非局部注意力机制也有效地提高了建模的效率,是病理学图像超分辨率重建的一种有效方法。
中图分类号:
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