BigFUSE: Global Context-Aware Image Fusion in Dual-View Light-Sheet Fluorescence Microscopy with Image Formation Prior
Light-sheet fluorescence microscopy (LSFM), a planar illumination technique that enables high-resolution imaging of samples, experiences defocused image quality caused by light scattering when photons propagate through thick tissues. To circumvent this issue, dualview imaging is helpful. It allows various sections of the specimen to be scanned ideally by viewing the sample from opposing orientations. Recent image fusion approaches can then be applied to determine in-focus pixels by comparing image qualities of two views locally and thus yield spatially inconsistent focus measures due to their limited field-of-view. Here, we propose BigFUSE, a global context-aware image fuser that stabilizes image fusion in LSFM by considering the global impact of photon propagation in the specimen while determining focus-defocus based on local image qualities. Inspired by the image formation prior in dual-view LSFM, image fusion is considered as estimating a focus-defocus boundary using Bayes Theorem, where (i) the effect of light scattering onto focus measures is included within Likelihood; and (ii) the spatial consistency regarding focus-defocus is imposed in Prior. The expectation-maximum algorithm is then adopted to estimate the focus-defocus boundary. Competitive experimental results show that BigFUSE is the first dual-view LSFM fuser that is able to exclude structured artifacts when fusing information, highlighting its abilities of automatic image fusion.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Local-Global Context-Aware and Structure-Preserving Image Super-Resolution
Diffusion models have recently achieved significant success in various image manipulation tasks, including image super-resolution and perceptual quality enhancement. Pretrained text-to-image models, such as Stable Diffus…
Image Super-ResolutionImage ManipulationTrimap-guided Feature Mining and Fusion Network for Natural Image Matting
Utilizing trimap guidance and fusing multi-level features are two important issues for trimap-based matting with pixel-level prediction. To utilize trimap guidance, most existing approaches simply concatenate trimaps and…
Image MattingCamContextI2V: Context-aware Controllable Video Generation
Recently, image-to-video (I2V) diffusion models have demonstrated impressive scene understanding and generative quality, incorporating image conditions to guide generation. However, these models primarily animate static …
DiversityScene UnderstandingVideo GenerationLocal Patches Meet Global Context: Scalable 3D Diffusion Priors for Computed Tomography Reconstruction
Diffusion models learn strong image priors that can be leveraged to solve inverse problems like medical image reconstruction. However, for real-world applications such as 3D Computed Tomography (CT) imaging, directly tra…
Image Reconstruction3D ReconstructionImage Generation3D GenerationFIAS: Feature Imbalance-Aware Medical Image Segmentation with Dynamic Fusion and Mixing Attention
With the growing application of transformer in computer vision, hybrid architecture that combine convolutional neural networks (CNNs) and transformers demonstrates competitive ability in medical image segmentation. Howev…
DecoderImage SegmentationMedical Image SegmentationSegmentation+1