A CANDLE for a deeper in vivo insight

被引:50
作者
Coupe, Pierrick [1 ,2 ,3 ]
Munz, Martin [3 ]
Manjon, Jose V. [4 ]
Ruthazer, Edward S. [3 ]
Collins, D. Louis [2 ,3 ]
机构
[1] CNRS, LaBRI, UMR 5800, F-33405 Talence, France
[2] McGill Univ, McConnell Brain Imaging Ctr, Montreal, PQ H3A 2B4, Canada
[3] McGill Univ, Montreal Neurol Inst, Montreal, PQ H3A 2B4, Canada
[4] Univ Politecn Valencia, Inst Aplicac Tecnol Informac & Comunicac Avanzada, Valencia 46022, Spain
基金
加拿大健康研究院;
关键词
Image denoising; Image enhancement; Multiphoton imaging; Confocal imaging; Non-local means filter; POISSON INTENSITY; NOISE; IMAGES; FILTER;
D O I
10.1016/j.media.2012.01.002
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
A new Collaborative Approach for eNhanced Denoising under Low-light Excitation (CANDLE) is introduced for the processing of 3D laser scanning multiphoton microscopy images. CANDLE is designed to be robust for low signal-to-noise ratio (SNR) conditions typically encountered when imaging deep in scattering biological specimens. Based on an optimized non-local means filter involving the comparison of filtered patches. CANDLE locally adapts the amount of smoothing in order to deal with the noise inhomogeneity inherent to laser scanning fluorescence microscopy images. An extensive validation on synthetic data, images acquired on microspheres and in vivo images is presented. These experiments show that the CANDLE filter obtained competitive results compared to a state-of-the-art method and a locally adaptive optimized non-local means filter, especially under low SNR conditions (PSNR < 8 dB). Finally, the deeper imaging capabilities enabled by the proposed filter are demonstrated on deep tissue in vivo images of neurons and fine axonal processes in the Xenopus tadpole brain. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:849 / 864
页数:16
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