Blind multi-Poissonian image deconvolution with sparse log-step gradient prior.

Blind color deconvolution, normalization, and classification of histological images using general super Gaussian priors and Bayesian inference

Gradient-based discriminative modeling for blind image deblurring

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 35821, “A Mobile Platform for AI-Powered Underwater Image Processing,” by Chenyu Zhou, University of Houston; Lily Walker, William & Mary; and Xin Fu, University of Houston, et al. The paper has not been peer reviewed. Copyright 2025 Offshore Technology Conference. _ Timely inspection of subsea infrastructure—particularly pipelines—is crucial for preventing oil spills and protecting the environment. This paper introduces a technology for offshore pipeline inspection centered on an autonomous robotic system equipped with underwater computer vision and edge-computing capabilities. The aim is to create a fast, cost-effective solution for inspecting underwater structures, enabling early detection of environmental hazards. Introduction Autonomous underwater vehicles (AUVs) have revolutionized the ability to explore and monitor marine environments. The cornerstone of AUV capabilities lies in their imaging systems, although the underwater environment poses unique and complex challenges for these systems. Additional image-processing and correction ability remains a goal for AUVs, creating computational demands that must be balanced against limited onboard power and processing capabilities (Fig. 1). Recently, advancements in deep learning have enabled deep neural networks (DNNs) to achieve remarkable success in solving a wide range of image-processing challenges, significantly surpassing the performance of traditional methods. In this research, the authors build upon foundational work in the literature to present a comprehensive workflow for adapting and deploying pretrained DNN models onto mobile computing platforms suitable for integration with AUVs. The described approach examines and documents the transition from powerful desktop systems to resource-constrained mobile platforms. Underwater Image Enhancement Motion blur is a common challenge in underwater image processing, especially when the camera is mounted on a moving platform such as an AUV. The lack of adequate lighting in underwater environments often requires longer exposure times, further exacerbating motion blur. The blurring process can be mathematically described as a convolution of the sharp image with a blur kernel, plus noise. If the blur kernel is known beforehand, the problem becomes one of nonblind deconvolution. However, when the blur kernel is unknown—common in dynamic underwater settings—it becomes a blind deconvolution problem. This problem is inherently ill-posed because multiple combinations of sharp images and blur kernels can explain the same blurred image. To resolve this ambiguity, assumptions or previous knowledge about the blur kernel or image structure typically are introduced. Traditional blind deconvolution methods often use iterative algorithms, refining the blur kernel and sharp image estimates over multiple steps. These classical approaches, however, often suffer from artifacts such as ringing and struggling with large motions or noise. To address these limitations, researchers have explored regularization techniques that incorporate priors to constrain the solution space. For example, sparsity priors in the temporal domain encourage smooth results, while Bayesian frameworks provide probabilistic formulations of the problem. One promising direction involves leveraging domain-specific priors for underwater imagery. By incorporating this property into optimization frameworks, researchers have developed more-effective and -robust blind deblurring methods tailored to underwater scenarios.

This paper proposes a new methodology to address the image quality problem encountered as the use of an unmanned aerial vehicle (UAV) in the field of bridge inspection increased. When inspecting a bridge, the image obtained from the UAV was degraded by various interference factors such as vibration, wind, and motion of UAV. Image quality degradation such as blur, noise, and low-resolution is a major obstacle in utilizing bridge inspection technology based on UAV. In particular, in the field of bridge inspection where damages must be accurately and quickly detected based on data obtained from UAV, these quality issues weaken the advantage of using UAVs by requiring re-take of images through re-flighting. Therefore, in this study, image quality assessment (IQA) based on local blur map (LBM) and image quality enhancement (IQE) using the variational Dirichlet (VD) kernel estimation were proposed as a solution to address the quality issues. First, image data was collected by setting different camera parameters for each bridge member. Second, a blur map was generated through discrete wavelet transform (DWT) and a new quality metric to measure the degree of blurriness was proposed. Third, for low-quality images with a large degree of blurriness, the blind kernel estimation and blind image deconvolution were performed to enhance the quality of images. In the validation tests, the proposed quality metric was applied to material image sets of bridge pier and deck taken from UAV, and its results were compared with those of other quality metrics based on singular value decomposition (SVD), sum of gray-intensity variance (SGV) and high-frequency multiscale fusion and sort transform (HiFST) methods. It was validated that the proposed IQA metric showed better classification performance on UAV images for bridge inspection through comparison with the classification results by human perception. In addition, by performing IQE, on average, 26% of blur was reduced, and the images with enhanced quality showed better damage detection performance through the deep learning model (i.e., mask and region-based convolutional neural network).

In this paper, we analyze the applicability of Constant Modulus Algorithm (CMA), one of the most widely used and tested blind equalization technique to blind image deconvolution. With a detailed mathematical analysis, we show that the strong correlation between the neighboring spatial locations found in natural images becomes a major constraint on the convergence of CMA. In order to overcome this constraint, we introduce a novel image pixel correlation model in relation with natural image statistics. Based on this model, a segmented blind image deconvolution through CMA is proposed. The robustness of the proposed algorithm with natural images is discussed in terms of efficiency and effectiveness.

This study addresses the blind image deconvolution which uses only blurred image and less point spread function (PSF) information to restore the original image. To identify the blind image it is a very important step for restoring the image. Therefore, the first step is to look for PSF model. In this paper, particle swarm optimization (PSO) is utilized to seek the unknown PSF. The objective function is based on the wavelet transform. It can identify the parameters of PSF exactly. Finally, the feasibility and validity of proposed algorithm are demonstrated by several simulations.

Spectrum extraction which is applied to extract one-dimensional (1D) astronomical spectroscopy from twodimensional (2D) multi-fiber spectrum images plays a key role in data processing of 2D optical fiber spectrum. In this paper, we have given a detailed comparison of spectrum extraction methods between the blind deconvolution and the non-blind deconvolution to find out their specialities. The simulated arc spectrum data is used in Comparative experiments. The results show that the blind deconvolution is better than the non-blind deconvolution not only at spectrum extraction result but also remarkably at signal noise ratio (SNR).

Blind image deconvolution (BID) is about recovering a latent image with sharp details from its blurred observation generated by the convolution with an unknown smoothing kernel. Recently, deep generative priors from untrained neural networks (NNs) have emerged as a promising deep learning approach for BID, with the benefit of being free of external training samples. However, existing untrained-NN-based BID methods may suffer from under-deblurring or overfitting. In this paper, we propose an ensemble approach to better exploit the priors from untrained NNs for BID, which aggregates the deblurring results of multiple untrained NNs for improvement. To enjoy both the effectiveness and computational efficiency in ensemble learning, the untrained NNs are designed with a specific shared-base and multi-head architecture. In addition, a kernel-centering layer is proposed for handling the shift ambiguity among different predictions during ensemble, which also improves the robustness of kernel prediction to the setting of the kernel size parameter. Extensive experiments show that the proposed approach noticeably outperforms both exiting dataset-free methods and dataset-based methods.

PurposeHow to model blind image deblurring that arises when a camera undergoes ego-motion while observing a static and close scene. In particular, this paper aims to detail how the blurry image can be restored under a sequence of the linear model of the point spread function (PSF) that are derived from the 6-degree of freedom (DOF) camera’s accurate path during the long exposure time.Design/methodology/approachThere are two existing techniques, namely, an estimation of the PSF and a blind image deconvolution. Based on online and short-period inertial measurement unit (IMU) self-calibration, this motion path has discretized a sequence of the uniform speed of 3-DOF rectilinear motion, which unites with a 3-DOF rotational motion to form a discrete 6-DOF camera’s path. These PSFs are evaluated through the discrete path, then combine with a blurry image to restoration through deconvolution.FindingsThis paper describes to build a hardware attachment, which is composed of a consumer camera, an inexpensive IMU and a 3-DOF motion mechanism to the best of the knowledge, together with experimental results demonstrating its overall effectiveness.Originality/valueFirst, the paper proposes that a high-precision 6-DOF motion platform periodically adjusts the speed of a three-axis rotational motion and a three-axis rectilinear motion in a short time to compensate the bias of the gyroscope and the accelerometer. Second, this paper establishes a model of 6-DOF motion and emphasizes on rotational motion, translational motion and scene depth motion. Third, this paper addresses a novel model of the discrete path that the motion during long exposure time is discretized at a uniform speed, then to estimate a sequence of PSFs.

We propose a new method of blind deconvolution (BD) suitable for the recovery of truncated blurred images. In practical cases we often encounter truncated images, but they are difficult to treat properly with conventional BD algorithms, since nontruncated blurred images are assumed. The proposed method is based on simulated annealing (SA), to which we introduce an adaptive masking process. After a truncated segment is cut out from the blurred image, the sharp edges surrounding the segment are removed by multiplying the initial window, whose shape is determined with the initial estimate of the point spread function (PSF). A more appropriate estimate for the PSF is obtained from the masked segment based on the SA algorithm, and according to the new estimate of the PSF, the shape of the window is varied. These procedures are iterated until we finally obtain the best possible estimate of the PSF. In the last step, the entire blurred image is deconvolved with the final estimate of the PSF to obtain the recovered image. The effectiveness of the proposed method is confirmed by computer simulation.

The segmentation of blurred images is of great importance. There have been several recent pieces of work to tackle this problem and to link the areas of image segmentation and image deconvolution in the case where the blur function κ is known or of known type, such as Gaussian, but not in the case where the blur function is not known due to a lack of robust blind deconvolution methods. Here we propose two variational models for simultaneous reconstruction and segmentation of blurred images with spatially invariant blur, without assuming a known blur or a known blur type. Based on our recent work in blind deconvolution, we present two solution methods for the segmentation of blurred images based on implicitly constrained image reconstruction and convex segmentation. The first method is aimed at obtaining a good quality segmentation while the other is aimed at improving the speed while retaining the quality. Our results demonstrate that, while existing models are capable of segmenting images corrupted by small amounts of blur, they begin to struggle when faced with heavy blur degradation or noise, due to the limitation of edge detectors or a lack of strict constraints. We demonstrate that our new algorithms are effective for segmenting blurred images without prior knowledge of the blur function, in the presence of noise and offer improved results for images corrupted by strong blur.

A weighted total variation algorithm was proposed for solving some blind deconvolution problems,and then we got the existence of energy functional minimum and educed corresponding thermal flow.In computer simulations,the algorithm performs well even when we have no more accurate prior knowledge about point spread function and the original true image,besides some other constrains.Experiments show that the proposed algorithm is more effective and performs better than the total variation blind deconvolution method proposed.

Coupling edge and texture detection using directional priors along with blind image deconvolution using an iterative alternating minimization algorithm gives an efficient deblurring. By assuming correct directional priors for edge detection in a constrained Maximum A Posteriori Estimation (MAP) accurate PSF estimation is achieved. The results are compared with existing blind deconvolution algorithms and are found to be more efficient.

As an ill-posed problem, multiframe blind super resolution imaging recovers a high resolution image from a group of low resolution images with some degradations when the information of blur kernel is limited. Note that the quality of the recovered image is influenced more by the accuracy of blur estimation than an advanced regularization. We study the traditional model of the multiframe super resolution and modify it for blind deblurring. Based on the analysis, we proposed two algorithms. The first one is based on the total variation blind deconvolution algorithm and formulated as a functional for optimization with the regularization of blur. Based on the alternating minimization and the gradient descent algorithm, the high resolution image and the unknown blur kernel are estimated iteratively. By using the median shift and add operator, the second algorithm is more robust to the outlier influence. The MSAA initialization simplifies the interpolation process to reconstruct the blurred high resolution image for blind deblurring and improves the accuracy of blind super resolution imaging. The experimental results demonstrate the superiority and accuracy of our novel algorithms.

We propose a novel linear blind deconvolution method for bi-level images. The proposed method seeks an optimal point spread function and two parameters that maximize a high order statistics based objective function. Unlike existing minimum entropy deconvolution and least squares minimization methods, the proposed method requires neither unrealistic assumption that the pixel values of a bi-level image are independently identically distributed samples of a random variable nor tuning of regularization parameters.We demonstrate the effectiveness of the proposed method in simulations and experiments.