Video Deblurring with Deconvolution and Aggregation Networks

In contrast to single-image deblurring, video deblurring has the advantage that neighbor frames can be utilized to deblur a target frame. However, existing video deblurring algorithms often fail to properly employ the neighbor frames, resulting in sub-optimal performance. In this paper, we propose a deconvolution and aggregation network (DAN) for video deblurring that utilizes the information of neighbor frames well. In DAN, both deconvolution and aggregation strategies are achieved through three sub-networks: the preprocessing network (PPN) and the alignment-based deconvolution network (ABDN) for the deconvolution scheme; the frame aggregation network (FAN) for the aggregation scheme. In the deconvolution part, blurry inputs are first preprocessed by the PPN with non-local operations. Then, the output frames from the PPN are deblurred by the ABDN based on the frame alignment. In the FAN, these deblurred frames from the deconvolution part are combined into a latent frame according to reliability maps which infer pixel-wise sharpness. The proper combination of three sub-networks can achieve favorable performance on video deblurring by using the neighbor frames suitably. In experiments, the proposed DAN was demonstrated to be superior to existing state-of-the-art methods through both quantitative and qualitative evaluations on the public datasets.

Fast Trajectory-Independent Model-Based Reconstruction Algorithm for Multi-Dimensional Magnetic Particle Imaging

Magnetic Particle Imaging (MPI) is a promising tomographic technique for visualizing the spatio-temporal distribution of superparamagnetic nanoparticles, with applications ranging from cancer detection to real-time cardiovascular monitoring. Traditional MPI reconstruction relies on either time-consuming calibration (measured system matrix) or model-based simulation of the forward operator. Recent developments have shown the applicability of Chebyshev polynomials to multi-dimensional Lissajous Field-Free Point (FFP) scans. This method is bound to the particular choice of sinusoidal scanning trajectories. In this paper, we present the first reconstruction on real 2D MPI data with a trajectory-independent model-based MPI reconstruction algorithm. We further develop the zero-shot Plug-and-Play (PnP) algorithm of the authors -- with automatic noise level estimation -- to address the present deconvolution problem, leveraging a state-of-the-art denoiser trained on natural images without retraining on MPI-specific data. We evaluate our method on the publicly available 2D FFP MPI dataset ``MPIdata: Equilibrium Model with Anisotropy", featuring scans of six phantoms acquired using a Bruker preclinical scanner. Moreover, we show reconstruction performed on custom data on a 2D scanner with additional high-frequency excitation field and partial data. Our results demonstrate strong reconstruction capabilities across different scanning scenarios -- setting a precedent for general-purpose, flexible model-based MPI reconstruction.

Super-Resolution with Structured Motion

We consider the limits of super-resolution using imaging constraints. Due to various theoretical and practical limitations, reconstruction-based methods have been largely restricted to small increases in resolution. In addition, motion-blur is usually seen as a nuisance that impedes super-resolution. We show that by using high-precision motion information, sparse image priors, and convex optimization, it is possible to increase resolution by large factors. A key operation in super-resolution is deconvolution with a box. In general, convolution with a box is not invertible. However, we obtain perfect reconstructions of sparse signals using convex optimization. We also show that motion blur can be helpful for super-resolution. We demonstrate that using pseudo-random motion it is possible to reconstruct a high-resolution target using a single low-resolution image. We present numerical experiments with simulated data and results with real data captured by a camera mounted on a computer controlled stage.

3D Gaussian Adaptive Reconstruction for Fourier Light-Field Microscopy

Compared to light-field microscopy (LFM), which enables high-speed volumetric imaging but suffers from non-uniform spatial sampling, Fourier light-field microscopy (FLFM) introduces sub-aperture division at the pupil plane, thereby ensuring spatially invariant sampling and enhancing spatial resolution. Conventional FLFM reconstruction methods, such as Richardson-Lucy (RL) deconvolution, exhibit poor axial resolution and signal degradation due to the ill-posed nature of the inverse problem. While data-driven approaches enhance spatial resolution by leveraging high-quality paired datasets or imposing structural priors, Neural Radiance Fields (NeRF)-based methods employ physics-informed self-supervised learning to overcome these limitations, yet they are hindered by substantial computational costs and memory demands. Therefore, we propose 3D Gaussian Adaptive Tomography (3DGAT) for FLFM, a 3D gaussian splatting based self-supervised learning framework that significantly improves the volumetric reconstruction quality of FLFM while maintaining computational efficiency. Experimental results indicate that our approach achieves higher resolution and improved reconstruction accuracy, highlighting its potential to advance FLFM imaging and broaden its applications in 3D optical microscopy.

Frequency-domain Learning with Kernel Prior for Blind Image Deblurring

While achieving excellent results on various datasets, many deep learning methods for image deblurring suffer from limited generalization capabilities with out-of-domain data. This limitation is likely caused by their dependence on certain domain-specific datasets. To address this challenge, we argue that it is necessary to introduce the kernel prior into deep learning methods, as the kernel prior remains independent of the image context. For effective fusion of kernel prior information, we adopt a rational implementation method inspired by traditional deblurring algorithms that perform deconvolution in the frequency domain. We propose a module called Frequency Integration Module (FIM) for fusing the kernel prior and combine it with a frequency-based deblurring Transfomer network. Experimental results demonstrate that our method outperforms state-of-the-art methods on multiple blind image deblurring tasks, showcasing robust generalization abilities. Source code will be available soon.

Efficient Chebyshev Reconstruction for the Anisotropic Equilibrium Model in Magnetic Particle Imaging

Magnetic Particle Imaging (MPI) is a tomographic imaging modality capable of real-time, high-sensitivity mapping of superparamagnetic iron oxide nanoparticles. Model-based image reconstruction provides an alternative to conventional methods that rely on a measured system matrix, eliminating the need for laborious calibration measurements. Nevertheless, model-based approaches must account for the complexities of the imaging chain to maintain high image quality. A recently proposed direct reconstruction method leverages weighted Chebyshev polynomials in the frequency domain, removing the need for a simulated system matrix. However, the underlying model neglects key physical effects, such as nanoparticle anisotropy, leading to distortions in reconstructed images. To mitigate these artifacts, an adapted direct Chebyshev reconstruction (DCR) method incorporates a spatially variant deconvolution step, significantly improving reconstruction accuracy at the cost of increased computational demands. In this work, we evaluate the adapted DCR on six experimental phantoms, demonstrating enhanced reconstruction quality in real measurements and achieving image fidelity comparable to or exceeding that of simulated system matrix reconstruction. Furthermore, we introduce an efficient approximation for the spatially variable deconvolution, reducing both runtime and memory consumption while maintaining accuracy. This method achieves computational complexity of O(N log N ), making it particularly beneficial for high-resolution and three-dimensional imaging. Our results highlight the potential of the adapted DCR approach for improving model-based MPI reconstruction in practical applications.

Deconver: A Deconvolutional Network for Medical Image Segmentation

While convolutional neural networks (CNNs) and vision transformers (ViTs) have advanced medical image segmentation, they face inherent limitations such as local receptive fields in CNNs and high computational complexity in ViTs. This paper introduces Deconver, a novel network that integrates traditional deconvolution techniques from image restoration as a core learnable component within a U-shaped architecture. Deconver replaces computationally expensive attention mechanisms with efficient nonnegative deconvolution (NDC) operations, enabling the restoration of high-frequency details while suppressing artifacts. Key innovations include a backpropagation-friendly NDC layer based on a provably monotonic update rule and a parameter-efficient design. Evaluated across four datasets (ISLES'22, BraTS'23, GlaS, FIVES) covering both 2D and 3D segmentation tasks, Deconver achieves state-of-the-art performance in Dice scores and Hausdorff distance while reducing computational costs (FLOPs) by up to 90% compared to leading baselines. By bridging traditional image restoration with deep learning, this work offers a practical solution for high-precision segmentation in resource-constrained clinical workflows. The project is available at https://github.com/pashtari/deconver.

Double Blind Imaging with Generative Modeling

Blind inverse problems in imaging arise from uncertainties in the system used to collect (noisy) measurements of images. Recovering clean images from these measurements typically requires identifying the imaging system, either implicitly or explicitly. A common solution leverages generative models as priors for both the images and the imaging system parameters (e.g., a class of point spread functions). To learn these priors in a straightforward manner requires access to a dataset of clean images as well as samples of the imaging system. We propose an AmbientGAN-based generative technique to identify the distribution of parameters in unknown imaging systems, using only unpaired clean images and corrupted measurements. This learned distribution can then be used in model-based recovery algorithms to solve blind inverse problems such as blind deconvolution. We successfully demonstrate our technique for learning Gaussian blur and motion blur priors from noisy measurements and show their utility in solving blind deconvolution with diffusion posterior sampling.

Joint multi-band deconvolution for $Euclid$ and $Vera$ $C.$ $Rubin$ images

With the advent of surveys like $Euclid$ and $Vera$ $C.$ $Rubin$, astrophysicists will have access to both deep, high-resolution images, and multi-band images. However, these two conditions are not simultaneously available in any single dataset. It is therefore vital to devise image deconvolution algorithms that exploit the best of the two worlds and that can jointly analyze datasets spanning a range of resolutions and wavelengths. In this work, we introduce a novel multi-band deconvolution technique aimed at improving the resolution of ground-based astronomical images by leveraging higher-resolution space-based observations. The method capitalizes on the fortunate fact that the $Vera$ $C.$ $Rubin$ $r$-, $i$-, and $z$-bands lie within the $Euclid$ $VIS$ band. The algorithm jointly deconvolves all the data to turn the $r$-, $i$-, and $z$-band $Vera$ $C.$ $Rubin$ images to the resolution of $Euclid$ by enabling us to leverage the correlations between the different bands. We also investigate the performance of deep learning-based denoising with DRUNet to further improve the results. We illustrate the effectiveness of our method in terms of resolution and morphology recovery, flux preservation, and generalization to different noise levels. This approach extends beyond the specific $Euclid$-$Rubin$ combination, offering a versatile solution to improve the resolution of ground-based images in multiple photometric bands by jointly using any space-based images with overlapping filters.

Ultrasound Phase Aberrated Point Spread Function Estimation with Convolutional Neural Network: Simulation Study

Ultrasound imaging systems rely on accurate point spread function (PSF) estimation to support advanced image quality enhancement techniques such as deconvolution and speckle reduction. Phase aberration, caused by sound speed inhomogeneity within biological tissue, is inevitable in ultrasound imaging. It distorts the PSF by increasing sidelobe level and introducing asymmetric amplitude, making PSF estimation under phase aberration highly challenging. In this work, we propose a deep learning framework for estimating phase-aberrated PSFs using U-Net and complex U-Net architectures, operating on RF and complex k-space data, respectively, with the latter demonstrating superior performance. Synthetic phase aberration data, generated using the near-field phase screen model, is employed to train the networks. We evaluate various loss functions and find that log-compressed B-mode perceptual loss achieves the best performance, accurately predicting both the mainlobe and near sidelobe regions of the PSF. Simulation results validate the effectiveness of our approach in estimating PSFs under varying levels of phase aberration.