Enhancing Fourier-based Doppler Resolution with Diffusion Models

• URL: http://arxiv.org/abs/2505.17567v1
• Date: Fri, 23 May 2025 07:27:19 GMT
• Title: Enhancing Fourier-based Doppler Resolution with Diffusion Models
• Authors: Denisa Qosja, Kilian Barth, Simon Wagner,
• Abstract summary: In radar systems, high resolution in the Doppler dimension is important for detecting slow-moving targets.<n>We leverage artificial intelligence to increase the Doppler resolution in range-Doppler maps.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In radar systems, high resolution in the Doppler dimension is important for detecting slow-moving targets as it allows for more distinct separation between these targets and clutter, or stationary objects. However, achieving sufficient resolution is constrained by hardware capabilities and physical factors, leading to the development of processing techniques to enhance the resolution after acquisition. In this work, we leverage artificial intelligence to increase the Doppler resolution in range-Doppler maps. Based on a zero-padded FFT, a refinement via the generative neural networks of diffusion models is achieved. We demonstrate that our method overcomes the limitations of traditional FFT, generating data where closely spaced targets are effectively separated.

Related papers

• One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillation [60.54811860967658]
  FluxSR is a novel one-step diffusion Real-ISR based on flow matching models.<n>First, we introduce Flow Trajectory Distillation (FTD) to distill a multi-step flow matching model into a one-step Real-ISR.<n>Second, to improve image realism and address high-frequency artifact issues in generated images, we propose TV-LPIPS as a perceptual loss.
  arXiv  Detail & Related papers  (2025-02-04T04:11:29Z)
• FAM Diffusion: Frequency and Attention Modulation for High-Resolution Image Generation with Stable Diffusion [63.609399000712905]
  Inference at a scaled resolution leads to repetitive patterns and structural distortions.<n>We propose two simple modules that combine to solve these issues.<n>Our method, coined Fam diffusion, can seamlessly integrate into any latent diffusion model and requires no additional training.
  arXiv  Detail & Related papers  (2024-11-27T17:51:44Z)
• Boosting Latent Diffusion with Perceptual Objectives [29.107038084215514]
  Latent diffusion models (LDMs) power state-of-the-art high-resolution generative image models.<n>We propose to leverage the internal features of the decoder to define a latent perceptual loss (LPL)<n>This loss encourages the models to create sharper and more realistic images.
  arXiv  Detail & Related papers  (2024-11-06T16:28:21Z)
• A Wavelet Diffusion GAN for Image Super-Resolution [7.986370916847687]
  Diffusion models have emerged as a superior alternative to generative adversarial networks (GANs) for high-fidelity image generation.<n>However, their real-time feasibility is hindered by slow training and inference speeds.<n>This study proposes a wavelet-based conditional Diffusion GAN scheme for Single-Image Super-Resolution.
  arXiv  Detail & Related papers  (2024-10-23T15:34:06Z)
• Effective Diffusion Transformer Architecture for Image Super-Resolution [63.254644431016345]
  We design an effective diffusion transformer for image super-resolution (DiT-SR) In practice, DiT-SR leverages an overall U-shaped architecture, and adopts a uniform isotropic design for all the transformer blocks. We analyze the limitation of the widely used AdaLN, and present a frequency-adaptive time-step conditioning module.
  arXiv  Detail & Related papers  (2024-09-29T07:14:16Z)
• Bring the Power of Diffusion Model to Defect Detection [0.0]
  diffusion probabilistic model (DDPM) is pre-trained to extract the features of denoising process to construct as a feature repository. The queried latent features are reconstructed and filtered to obtain high-dimensional DDPM features. Experiment results demonstrate that our method achieves competitive results on several industrial datasets.
  arXiv  Detail & Related papers  (2024-08-25T14:28:49Z)
• Diffusion Model is a Good Pose Estimator from 3D RF-Vision [32.72703340013302]
  Human pose estimation (HPE) from Radio Frequency vision (RF-vision) performs human sensing using RF signals. mmWave radar has emerged as a promising RF-vision sensor, providing radar point clouds by processing RF signals. This work proposes mmDiff, a novel diffusion-based pose estimator tailored for noisy radar data.
  arXiv  Detail & Related papers  (2024-03-24T15:39:52Z)
• Misalignment-Robust Frequency Distribution Loss for Image Transformation [51.0462138717502]
  This paper aims to address a common challenge in deep learning-based image transformation methods, such as image enhancement and super-resolution. We introduce a novel and simple Frequency Distribution Loss (FDL) for computing distribution distance within the frequency domain. Our method is empirically proven effective as a training constraint due to the thoughtful utilization of global information in the frequency domain.
  arXiv  Detail & Related papers  (2024-02-28T09:27:41Z)
• Diffusion-Based Particle-DETR for BEV Perception [94.88305708174796]
  Bird-Eye-View (BEV) is one of the most widely-used scene representations for visual perception in Autonomous Vehicles (AVs) Recent diffusion-based methods offer a promising approach to uncertainty modeling for visual perception but fail to effectively detect small objects in the large coverage of the BEV. Here, we address this problem by combining the diffusion paradigm with current state-of-the-art 3D object detectors in BEV.
  arXiv  Detail & Related papers  (2023-12-18T09:52:14Z)
• Diffusion Models Without Attention [110.5623058129782]
  Diffusion State Space Model (DiffuSSM) is an architecture that supplants attention mechanisms with a more scalable state space model backbone. Our focus on FLOP-efficient architectures in diffusion training marks a significant step forward.
  arXiv  Detail & Related papers  (2023-11-30T05:15:35Z)
• Frequency Compensated Diffusion Model for Real-scene Dehazing [6.105813272271171]
  We consider a dehazing framework based on conditional diffusion models for improved generalization to real haze. The proposed dehazing diffusion model significantly outperforms state-of-the-art methods on real-world images.
  arXiv  Detail & Related papers  (2023-08-21T06:50:44Z)
• Consistent Direct Time-of-Flight Video Depth Super-Resolution [9.173767380836852]
  Direct time-of-flight (dToF) sensors are promising for next-generation on-device 3D sensing. We propose the first multi-frame fusion scheme to mitigate the spatial ambiguity resulting from the low-resolution dToF imaging. We introduce DyDToF, the first synthetic RGB-dToF video dataset that features dynamic objects and a realistic dToF simulator.
  arXiv  Detail & Related papers  (2022-11-16T04:16:20Z)

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.