Blurry-Edges: Photon-Limited Depth Estimation from Defocused Boundaries

• URL: http://arxiv.org/abs/2503.23606v1
• Date: Sun, 30 Mar 2025 22:17:00 GMT
• Title: Blurry-Edges: Photon-Limited Depth Estimation from Defocused Boundaries
• Authors: Wei Xu, Charles James Wagner, Junjie Luo, Qi Guo,
• Abstract summary: We present a novel approach to robustly measure object depths from photon-limited images along the defocused boundaries.<n>It is based on a new image patch representation, Blurry-Edges, that explicitly stores and visualizes a rich set of low-level patch information, including boundaries, color, and smoothness.
• Score: 9.723762227632378
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Extracting depth information from photon-limited, defocused images is challenging because depth from defocus (DfD) relies on accurate estimation of defocus blur, which is fundamentally sensitive to image noise. We present a novel approach to robustly measure object depths from photon-limited images along the defocused boundaries. It is based on a new image patch representation, Blurry-Edges, that explicitly stores and visualizes a rich set of low-level patch information, including boundaries, color, and smoothness. We develop a deep neural network architecture that predicts the Blurry-Edges representation from a pair of differently defocused images, from which depth can be calculated using a closed-form DfD relation we derive. The experimental results on synthetic and real data show that our method achieves the highest depth estimation accuracy on photon-limited images compared to a broad range of state-of-the-art DfD methods.

Related papers

• Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion [51.69876947593144]
  Existing methods for depth completion operate in tightly constrained settings.<n>Inspired by advances in monocular depth estimation, we reframe depth completion as an image-conditional depth map generation.<n>Marigold-DC builds on a pretrained latent diffusion model for monocular depth estimation and injects the depth observations as test-time guidance.
  arXiv  Detail & Related papers  (2024-12-18T00:06:41Z)
• Robust Depth Enhancement via Polarization Prompt Fusion Tuning [112.88371907047396]
  We present a framework that leverages polarization imaging to improve inaccurate depth measurements from various depth sensors. Our method first adopts a learning-based strategy where a neural network is trained to estimate a dense and complete depth map from polarization data and a sensor depth map from different sensors. To further improve the performance, we propose a Polarization Prompt Fusion Tuning (PPFT) strategy to effectively utilize RGB-based models pre-trained on large-scale datasets.
  arXiv  Detail & Related papers  (2024-04-05T17:55:33Z)
• Deep Phase Coded Image Prior [34.84063452418995]
  Phase-coded imaging is a method to tackle tasks such as passive depth estimation and extended depth of field.<n>Most of the current deep learning-based methods for depth estimation or all-in-focus imaging require a training dataset with high-quality depth maps.<n>We propose a new method named "Deep Phase Coded Image Prior" (DPCIP) for jointly recovering the depth map and all-in-focus image.
  arXiv  Detail & Related papers  (2024-04-05T05:58:40Z)
• Depth and DOF Cues Make A Better Defocus Blur Detector [27.33757097343283]
  Defocus blur detection (DBD) separates in-focus and out-of-focus regions in an image. Previous approaches mistakenly mistook homogeneous areas in focus for defocus blur regions. We propose an approach called D-DFFNet, which incorporates depth and DOF cues in an implicit manner.
  arXiv  Detail & Related papers  (2023-06-20T07:03:37Z)
• Fully Self-Supervised Depth Estimation from Defocus Clue [79.63579768496159]
  We propose a self-supervised framework that estimates depth purely from a sparse focal stack. We show that our framework circumvents the needs for the depth and AIF image ground-truth, and receives superior predictions.
  arXiv  Detail & Related papers  (2023-03-19T19:59:48Z)
• End-to-end Learning for Joint Depth and Image Reconstruction from Diffracted Rotation [10.896567381206715]
  We propose a novel end-to-end learning approach for depth from diffracted rotation. Our approach requires a significantly less complex model and less training data, yet it is superior to existing methods in the task of monocular depth estimation.
  arXiv  Detail & Related papers  (2022-04-14T16:14:37Z)
• Adaptive Weighted Guided Image Filtering for Depth Enhancement in Shape-From-Focus [28.82811159799952]
  Shape from focus (SFF) techniques cannot preserve depth edges and fine structural details from a sequence of multi-focus images. A novel depth enhancement algorithm for the SFF based on an adaptive weighted guided image filtering (AWGIF) is proposed.
  arXiv  Detail & Related papers  (2022-01-18T08:52:26Z)
• Single image deep defocus estimation and its applications [82.93345261434943]
  We train a deep neural network to classify image patches into one of the 20 levels of blurriness. The trained model is used to determine the patch blurriness which is then refined by applying an iterative weighted guided filter. The result is a defocus map that carries the information of the degree of blurriness for each pixel.
  arXiv  Detail & Related papers  (2021-07-30T06:18:16Z)
• Deep Multi-Scale Feature Learning for Defocus Blur Estimation [10.455763145066168]
  This paper presents an edge-based defocus blur estimation method from a single defocused image. We first distinguish edges that lie at depth discontinuities (called depth edges, for which the blur estimate is ambiguous) from edges that lie at approximately constant depth regions (called pattern edges, for which the blur estimate is well-defined). We estimate the defocus blur amount at pattern edges only, and explore an scheme based on guided filters that prevents data propagation across the detected depth edges to obtain a dense blur map with well-defined object boundaries.
  arXiv  Detail & Related papers  (2020-09-24T20:36:40Z)
• Defocus Blur Detection via Depth Distillation [64.78779830554731]
  We introduce depth information into DBD for the first time. In detail, we learn the defocus blur from ground truth and the depth distilled from a well-trained depth estimation network. Our approach outperforms 11 other state-of-the-art methods on two popular datasets.
  arXiv  Detail & Related papers  (2020-07-16T04:58:09Z)
• Depth Completion Using a View-constrained Deep Prior [73.21559000917554]
  Recent work has shown that the structure of convolutional neural networks (CNNs) induces a strong prior that favors natural images. This prior, known as a deep image prior (DIP), is an effective regularizer in inverse problems such as image denoising and inpainting. We extend the concept of the DIP to depth images. Given color images and noisy and incomplete target depth maps, we reconstruct a depth map restored by virtue of using the CNN network structure as a prior.
  arXiv  Detail & Related papers  (2020-01-21T21:56:01Z)

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.