Temporal Spatial-Adaptive Interpolation with Deformable Refinement for Electron Microscopic Images

• URL: http://arxiv.org/abs/2101.06771v1
• Date: Sun, 17 Jan 2021 20:22:52 GMT
• Title: Temporal Spatial-Adaptive Interpolation with Deformable Refinement for Electron Microscopic Images
• Authors: Zejin Wang, Guodong Sun, Lina Zhang, Guoqing Li, Hua Han
• Abstract summary: Existing electron microscopic (EM) images suffer from unstable image quality, low PSNR, and disorderly deformation. We propose a novel framework for EM images that progressively synthesizes interpolated features in a coarse-to-fine manner. Experimental results demonstrate the superior performance of our approach compared to previous works.
• Score: 10.26899472047613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, flow-based methods have achieved promising success in video frame interpolation. However, electron microscopic (EM) images suffer from unstable image quality, low PSNR, and disorderly deformation. Existing flow-based interpolation methods cannot precisely compute optical flow for EM images since only predicting each position's unique offset. To overcome these problems, we propose a novel interpolation framework for EM images that progressively synthesizes interpolated features in a coarse-to-fine manner. First, we extract missing intermediate features by the proposed temporal spatial-adaptive (TSA) interpolation module. The TSA interpolation module aggregates temporal contexts and then adaptively samples the spatial-related features with the proposed residual spatial adaptive block. Second, we introduce a stacked deformable refinement block (SDRB) further enhance the reconstruction quality, which is aware of the matching positions and relevant features from input frames with the feedback mechanism. Experimental results demonstrate the superior performance of our approach compared to previous works, both quantitatively and qualitatively.

Related papers

• Multipath Interference Suppression in Indirect Time-of-Flight Imaging via a Novel Compressed Sensing Framework [0.2710246456535607]
  We propose a novel compressed sensing method to improve the depth reconstruction accuracy and multi-target separation capability of indirect Time-of-Flight (iToF) systems.<n>Our method operates with a single modulation frequency and constructs the sensing matrix using multiple phase shifts and narrow-duty-cycle continuous waves.<n> Experimental results demonstrate that the proposed method outperforms traditional approaches in both reconstruction accuracy and robustness, without requiring any additional hardware changes.
  arXiv  Detail & Related papers  (2025-07-24T03:54:27Z)
• Diffusion Models for Solving Inverse Problems via Posterior Sampling with Piecewise Guidance [52.705112811734566]
  A novel diffusion-based framework is introduced for solving inverse problems using a piecewise guidance scheme.<n>The proposed method is problem-agnostic and readily adaptable to a variety of inverse problems.<n>The framework achieves a reduction in inference time of (25%) for inpainting with both random and center masks, and (23%) and (24%) for (4times) and (8times) super-resolution tasks.
  arXiv  Detail & Related papers  (2025-07-22T19:35:14Z)
• Solving Inverse Problems with FLAIR [59.02385492199431]
  Flow-based latent generative models are able to generate images with remarkable quality, even enabling text-to-image generation.<n>We present FLAIR, a novel training free variational framework that leverages flow-based generative models as a prior for inverse problems.<n>Results on standard imaging benchmarks demonstrate that FLAIR consistently outperforms existing diffusion- and flow-based methods in terms of reconstruction quality and sample diversity.
  arXiv  Detail & Related papers  (2025-06-03T09:29:47Z)
• Unaligned RGB Guided Hyperspectral Image Super-Resolution with Spatial-Spectral Concordance [18.045877106333766]
  Hyperspectral images super-resolution aims to improve the spatial resolution, yet its performance is often limited at high-resolution ratios.<n>We introduce a framework for unaligned reference RGB guided HSI SR to address the issues of inaccurate alignment and poor interactivity.<n>Our method outperforms state-of-the-art approaches on both quantitative and qualitative evaluations.
  arXiv  Detail & Related papers  (2025-05-04T13:29:31Z)
• Continuous Filtered Backprojection by Learnable Interpolation Network [48.52134830162271]
  In this study, we propose a novel deep learning model, named Leanable-Interpolation-based FBP or LInFBP.<n>In the proposed LInFBP, we formulate every local piece of the latent continuous function of discrete sinogram data as a linear combination of selected basis functions.<n>Then, the learned latent continuous function is exploited for in backprojection step, which first time takes the advantage of deep learning for the in FBP.
  arXiv  Detail & Related papers  (2025-05-03T09:50:27Z)
• Empowering Snapshot Compressive Imaging: Spatial-Spectral State Space Model with Across-Scanning and Local Enhancement [51.557804095896174]
  We introduce a State Space Model with Across-Scanning and Local Enhancement, named ASLE-SSM, that employs a Spatial-Spectral SSM for global-local balanced context encoding and cross-channel interaction promoting. Experimental results illustrate ASLE-SSM's superiority over existing state-of-the-art methods, with an inference speed 2.4 times faster than Transformer-based MST and saving 0.12 (M) of parameters.
  arXiv  Detail & Related papers  (2024-08-01T15:14:10Z)
• Deformable Feature Alignment and Refinement for Moving Infrared Dim-small Target Detection [17.765101100010224]
  We propose a Deformable Feature Alignment and Refinement (DFAR) method based on deformable convolution to explicitly use motion context in both the training and inference stages. The proposed DFAR method achieves the state-of-the-art performance on two benchmark datasets including DAUB and IRDST.
  arXiv  Detail & Related papers  (2024-07-10T00:42:25Z)
• DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection [52.74152717667157]
  We propose a lightweight module called Dual Attention Module (DAM) for capturing cross-dimension interaction relationships in-temporal skeletal data. It employs the frame attention mechanism to identify the most significant frames and the skeleton attention mechanism to capture broader relationships across fixed partitions with minimal parameters and flops.
  arXiv  Detail & Related papers  (2024-06-05T06:18:03Z)
• AID: Attention Interpolation of Text-to-Image Diffusion [64.87754163416241]
  We introduce a training-free technique named Attention Interpolation via Diffusion (AID) AID fuses the interpolated attention with self-attention to boost fidelity. We also present a variant, Conditional-guided Attention Interpolation via Diffusion (AID), that considers as a condition-dependent generative process.
  arXiv  Detail & Related papers  (2024-03-26T17:57:05Z)
• Motion-Aware Video Frame Interpolation [49.49668436390514]
  We introduce a Motion-Aware Video Frame Interpolation (MA-VFI) network, which directly estimates intermediate optical flow from consecutive frames. It not only extracts global semantic relationships and spatial details from input frames with different receptive fields, but also effectively reduces the required computational cost and complexity.
  arXiv  Detail & Related papers  (2024-02-05T11:00:14Z)
• Physics-Inspired Degradation Models for Hyperspectral Image Fusion [61.743696362028246]
  Most fusion methods solely focus on the fusion algorithm itself and overlook the degradation models. We propose physics-inspired degradation models (PIDM) to model the degradation of LR-HSI and HR-MSI. Our proposed PIDM can boost the fusion performance of existing fusion methods in practical scenarios.
  arXiv  Detail & Related papers  (2024-02-04T09:07:28Z)
• Support Vector Machine Guided Reproducing Kernel Particle Method for Image-Based Modeling of Microstructures [1.2163458046014015]
  The proposed method is guided by the Support Vector Machine (SVM) classification, offering an effective approach for discretizing microstructural images. An Interface-Modified Reproducing Kernel Particle Method (IM-RKPM) is proposed for appropriate approximations of weak discontinuities across material interfaces.
  arXiv  Detail & Related papers  (2023-05-23T19:00:09Z)
• Meta-Interpolation: Time-Arbitrary Frame Interpolation via Dual Meta-Learning [65.85319901760478]
  We consider processing different time-steps with adaptively generated convolutional kernels in a unified way with the help of meta-learning. We develop a dual meta-learned frame framework to synthesize intermediate frames with the guidance of context information and optical flow.
  arXiv  Detail & Related papers  (2022-07-27T17:36:23Z)
• Neural UpFlow: A Scene Flow Learning Approach to Increase the Apparent Resolution of Particle-Based Liquids [0.6882042556551611]
  We present a novel up-resing technique for generating high-resolution liquids based on scene flow estimation using deep neural networks. Our approach infers and synthesizes small- and large-scale details solely from a low-resolution particle-based liquid simulation.
  arXiv  Detail & Related papers  (2021-06-09T15:36:23Z)
• Weighted Encoding Based Image Interpolation With Nonlocal Linear Regression Model [8.013127492678272]
  In image super-resolution, the low-resolution image is directly down-sampled from its high-resolution counterpart without blurring and noise. To address this problem, we propose a novel image model based on sparse representation. New approach to learn adaptive sub-dictionary online instead of clustering.
  arXiv  Detail & Related papers  (2020-03-04T03:20:21Z)
• Kullback-Leibler Divergence-Based Fuzzy $C$-Means Clustering Incorporating Morphological Reconstruction and Wavelet Frames for Image Segmentation [152.609322951917]
  We come up with a Kullback-Leibler (KL) divergence-based Fuzzy C-Means (FCM) algorithm by incorporating a tight wavelet frame transform and a morphological reconstruction operation. The proposed algorithm works well and comes with better segmentation performance than other comparative algorithms.
  arXiv  Detail & Related papers  (2020-02-21T05:19:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.