ABSTRACT This paper presents a novel triple‐color watermarking algorithm that leverages the elliptical monogenic wavelet transform (EMWT) and singular value decomposition (SVD) for robust and secure watermark embedding. The proposed method first encrypts the host image using a random matrix. Subsequently, both the host and watermark images undergo preprocessing techniques, including EMWT, discrete wavelet transform (DWT), discrete cosine transform (DCT), and SVD, to obtain the diagonal matrices required for embedding. For enhanced security, the watermark images are encrypted using the Arnold method with phase and linear representation embedding, where the second key is derived from the host image via Fourier transform. The watermarked image is then generated by applying the corresponding inverse transforms. Experimental results demonstrate the algorithm's excellent embedding and extraction performance on color images and icons with characters. Notably, the proposed method exhibits strong robustness against various attacks, including non‐geometric attacks (e.g., noise, filtering), geometric attacks (e.g., rotation, cropping), and mixed attacks, outperforming existing color watermarking methods in terms of anti‐attack ability and robustness.

Reconstructing neurons from large electron microscopy (EM) datasets for connectomic analysis presents a significant challenge, particularly in segmenting neurons of complex morphologies. Previous deep learning-based neuron segmentation methods often rely on pixel-level image context and produce extensive oversegmented fragments. Detecting these split errors and merging the split neuron segments are non-trivial for various neurons in a large-scale EM data volume. In this work, we exploit multimodal features in the full workflow of automatic neuron proofreading. We propose a novel connection point detection network that utilizes both global 3D morphological features and high-resolution local image context to extract candidate segment pairs from massive adjacent segments. To effectively fuse the 3D morphological feature and the dense image features from very different scales, we design a proposal-based image feature sampling to improve the efficiency of multimodal cross-attentions. Integrating the connection point detection network with our connectivity prediction network which also utilizes multimodal features, we make a fully automatic neuron segment merging pipeline, closely imitating human proofreading. Comprehensive experimental results verify the effectiveness of the proposed modules and demonstrate the robustness of the entire pipeline in large-scale neuron reconstruction. The code and data are available at https://github.com/Levishery/Neuron-Segment-Connection-Prediction.

The current neuron reconstruction pipeline for electron microscopy (EM) data usually includes automatic image segmentation followed by extensive human expert proofreading. In this work, we aim to reduce human workload by predicting connectivity between over-segmented neuron pieces, taking both microscopy image and 3D morphology features into account, similar to human proofreading workflow. To this end, we first construct a dataset, named FlyTracing, that contains millions of pairwise connections of segments expanding the whole fly brain, which is three orders of magnitude larger than existing datasets for neuron segment connection. To learn sophisticated biological imaging features from the connectivity annotations, we propose a novel connectivity-aware contrastive learning method to generate dense volumetric EM image embedding. The learned embeddings can be easily incorporated with any point or voxel-based morphological representations for automatic neuron tracing. Extensive comparisons of different combination schemes of image and morphological representation in identifying split errors across the whole fly brain demonstrate the superiority of the proposed approach, especially for the locations that contain severe imaging artifacts, such as section missing and misalignment. The dataset and code are available at https://github.com/Levishery/Flywire-Neuron-Tracing.