Learning Point Spread Function Invertibility Assessment for Image Deconvolution
- URL: http://arxiv.org/abs/2405.16343v2
- Date: Tue, 25 Jun 2024 19:35:10 GMT
- Title: Learning Point Spread Function Invertibility Assessment for Image Deconvolution
- Authors: Romario Gualdrón-Hurtado, Roman Jacome, Sergio Urrea, Henry Arguello, Luis Gonzalez,
- Abstract summary: We propose a metric that employs a non-linear approach to learn the invertibility of an arbitrary PSF using a neural network.
A lower discrepancy between the mapped PSF and a unit impulse indicates a higher likelihood of successful inversion by a DL network.
- Score: 14.062542012968313
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep-learning (DL)-based image deconvolution (ID) has exhibited remarkable recovery performance, surpassing traditional linear methods. However, unlike traditional ID approaches that rely on analytical properties of the point spread function (PSF) to achieve high recovery performance - such as specific spectrum properties or small conditional numbers in the convolution matrix - DL techniques lack quantifiable metrics for evaluating PSF suitability for DL-assisted recovery. Aiming to enhance deconvolution quality, we propose a metric that employs a non-linear approach to learn the invertibility of an arbitrary PSF using a neural network by mapping it to a unit impulse. A lower discrepancy between the mapped PSF and a unit impulse indicates a higher likelihood of successful inversion by a DL network. Our findings reveal that this metric correlates with high recovery performance in DL and traditional methods, thereby serving as an effective regularizer in deconvolution tasks. This approach reduces the computational complexity over conventional condition number assessments and is a differentiable process. These useful properties allow its application in designing diffractive optical elements through end-to-end (E2E) optimization, achieving invertible PSFs, and outperforming the E2E baseline framework.
Related papers
- PseudoNeg-MAE: Self-Supervised Point Cloud Learning using Conditional Pseudo-Negative Embeddings [55.55445978692678]
PseudoNeg-MAE is a self-supervised learning framework that enhances global feature representation of point cloud mask autoencoders.
We show that PseudoNeg-MAE achieves state-of-the-art performance on the ModelNet40 and ScanObjectNN datasets.
arXiv Detail & Related papers (2024-09-24T07:57:21Z) - Generalizable Non-Line-of-Sight Imaging with Learnable Physical Priors [52.195637608631955]
Non-line-of-sight (NLOS) imaging has attracted increasing attention due to its potential applications.
Existing NLOS reconstruction approaches are constrained by the reliance on empirical physical priors.
We introduce a novel learning-based solution, comprising two key designs: Learnable Path Compensation (LPC) and Adaptive Phasor Field (APF)
arXiv Detail & Related papers (2024-09-21T04:39:45Z) - Layer-wise Feedback Propagation [53.00944147633484]
We present Layer-wise Feedback Propagation (LFP), a novel training approach for neural-network-like predictors.
LFP assigns rewards to individual connections based on their respective contributions to solving a given task.
We demonstrate its effectiveness in achieving comparable performance to gradient descent on various models and datasets.
arXiv Detail & Related papers (2023-08-23T10:48:28Z) - Implicit Stochastic Gradient Descent for Training Physics-informed
Neural Networks [51.92362217307946]
Physics-informed neural networks (PINNs) have effectively been demonstrated in solving forward and inverse differential equation problems.
PINNs are trapped in training failures when the target functions to be approximated exhibit high-frequency or multi-scale features.
In this paper, we propose to employ implicit gradient descent (ISGD) method to train PINNs for improving the stability of training process.
arXiv Detail & Related papers (2023-03-03T08:17:47Z) - FIT: A Metric for Model Sensitivity [1.2622086660704197]
We propose FIT, which combines the Fisher information with a model of quantization.
We find that FIT can estimate the final performance of a network without retraining.
FIT is fast to compute when compared to existing methods, demonstrating favourable convergence properties.
arXiv Detail & Related papers (2022-10-16T10:25:29Z) - Parametric Level-sets Enhanced To Improve Reconstruction (PaLEnTIR) [0.0]
We introduce PaLEnTIR, a significantly enhanced parametric level-set (PaLS) method addressing the restoration and reconstruction of piecewise constant objects.
Our key contribution involves a unique PaLS formulation utilizing a single level-set function to restore scenes containing multi-contrast piecewise-constant objects.
arXiv Detail & Related papers (2022-04-21T00:03:44Z) - Tensor and Matrix Low-Rank Value-Function Approximation in Reinforcement Learning [11.317136648551536]
Value-function approximation is a central problem in Reinforcement Learning (RL)
This paper puts forth a parsimonious non-parametric approach, where we use low-rank algorithms to estimate the VF matrix in an online and model-free fashion.
As VFs tend to be multi-dimensional, we propose replacing the classical VF matrix representation with a tensor representation and, then, use the PARAFAC decomposition to design an online model-free tensor low-rank algorithm.
arXiv Detail & Related papers (2022-01-21T00:13:54Z) - Learning Discriminative Shrinkage Deep Networks for Image Deconvolution [122.79108159874426]
We propose an effective non-blind deconvolution approach by learning discriminative shrinkage functions to implicitly model these terms.
Experimental results show that the proposed method performs favorably against the state-of-the-art ones in terms of efficiency and accuracy.
arXiv Detail & Related papers (2021-11-27T12:12:57Z) - Implicit Under-Parameterization Inhibits Data-Efficient Deep
Reinforcement Learning [97.28695683236981]
More gradient updates decrease the expressivity of the current value network.
We demonstrate this phenomenon on Atari and Gym benchmarks, in both offline and online RL settings.
arXiv Detail & Related papers (2020-10-27T17:55:16Z)
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.