Blur Map Research Articles

Street view images blur detection

<div><p>Blurred regions in images can hinder visual analysis and have a notable impact on applications such as navigation systems and virtual tours. Many existing approaches in the literature assume the presence of blurred regions in an image and process the entire image, even when no blurred regions are actually present. This approach leads to unnecessary computational overhead, resulting in inefficiency and resource consumption. In this paper, we introduce a Street-view images Blur Detection Network (SBDNet), consisting of two interconnected subnetworks: the Classifier network and the Identifier network. The Classifier network is responsible for categorizing street-view images as either blurred or not blurred. Once the Classifier network determines that an image is blurred, the Identifier network is then activated to estimate the specific areas that are blurred within the image. High-level semantic features from the Classifier network are used to construct the blur map estimation in the Identifier network, when necessary. The algorithm was trained and evaluated using the Street-View Blur Images dataset (SVBI) and three publicly available blur detection datasets: CUHK, DUT, SZU-BD. Our quantitative and qualitative results demonstrate that SBDNet competes with state of the arts in blur map estimation.</p></div>

Image defocus deblurring method based on gradient difference of boundary neighborhood

BackgroundFor static scenes with multiple depth layers, the existing defocused image deblurring methods have the problems of edge ringing artifacts or insufficient deblurring degree due to inaccurate estimation of blur amount, In addition, the prior knowledge in non blind deconvolution is not strong, which leads to image detail recovery challenge. MethodsTo this end, this paper proposes a blur map estimation method for defocused images based on the gradient difference of the boundary neighborhood, which uses the gradient difference of the boundary neighborhood to accurately obtain the amount of blurring, thus preventing boundary ringing artifacts. Then, the obtained blur map is used for blur detection to determine whether the image needs to be deblurred, thereby improving the efficiency of deblurring without manual intervention and judgment. Finally, a non blind deconvolution algorithm is designed to achieve image deblurring based on the blur amount selection strategy and sparse prior. ResultsExperimental results show that our method improves PSNR and SSIM by an average of 4.6% and 7.3%, respectively, compared to existing methods. ConclusionsExperimental results show that our method outperforms existing methods. Compared with existing methods, our method can better solve the problems of boundary ringing artifacts and detail information preservation in defocused image deblurring.

Towards Improved Heliosphere Sky Map Estimation with Theseus

The Interstellar Boundary Explorer (IBEX) satellite has been in orbit since 2008 and detects energy-resolved energetic neutral atoms (ENAs) originating from the heliosphere. Different regions of the heliosphere generate ENAs at different rates. It is of scientific interest to take the data collected by IBEX and estimate spatial maps of heliospheric ENA rates (referred to as sky maps) at higher resolutions than before. These sky maps will subsequently be used to discern between competing theories of heliosphere properties that are not currently possible. The data IBEX collects present challenges to sky map estimation. The two primary challenges are noisy and irregularly spaced data collection and the IBEX instrumentation’s point spread function. In essence, the data collected by IBEX are both noisy and biased for the underlying sky map of inferential interest. In this article, we present a two-stage sky map estimation procedure called Theseus. In Stage 1, Theseus estimates a blurred sky map from the noisy and irregularly spaced data using an ensemble approach that leverages projection pursuit regression and generalized additive models. In Stage 2, Theseus deblurs the sky map by deconvolving the PSF with the blurred map using regularization. Unblurred sky map uncertainties are computed via bootstrapping. We compare Theseus to a method closely related to the one operationally used today by the IBEX Science Operation Center (ISOC) on both simulated and real data. Theseus outperforms ISOC in nearly every considered metric on simulated data, indicating that Theseus is an improvement over the current state of the art.

A Motion Deblurring Network for Enhancing UAV Image Quality in Bridge Inspection

Unmanned aerial vehicles (UAVs) have been increasingly utilized for facility safety inspections due to their superior safety, cost effectiveness, and inspection accuracy compared to traditional manpower-based methods. High-resolution images captured by UAVs directly contribute to identifying and quantifying structural defects on facility exteriors, making image quality a critical factor in achieving accurate results. However, motion blur induced by external factors such as vibration, low light conditions, and wind during UAV operation significantly degrades image quality, leading to inaccurate defect detection and quantification. To address this issue, this research proposes a deblurring network using a Generative Adversarial Network (GAN) to eliminate the motion blur effect in UAV images. The GAN-based motion deblur network represents an image inpainting method that leverages generative models to correct blurry artifacts, thereby generating clear images. Unlike previous studies, this proposed approach incorporates deblur and blur learning modules to realistically generate blur images required for training the generative models. The UAV images processed using the motion deblur network are evaluated using a quality assessment method based on local blur map and other well-known image quality assessment (IQA) metrics. Moreover, in the experiment of crack detection utilizing the object detection system, improved detection results are observed when using enhanced images. Overall, this research contributes to improving the quality and accuracy of facility safety inspections conducted with UAV-based inspections by effectively addressing the challenges associated with motion blur effects in UAV-captured images.

Perception-guided defocus blur detection based on SVD feature

Defocus blur detection aims to identify out-of-focus regions in a single image. Although defocus blur detection has attracted more and more attention in recent years, it still faces some challenges. In particular, the in-focus regions with low contrast are easily misidentified as out-of-focus regions. To address this problem, a perception-guided defocus blur detection method is proposed to estimate defocus blur amounts at edge locations from a single image based on the singular value decomposition (SVD) features. Inspired by the fact that blurring the clear region produces significant changes in SVD domain as compared with re-blurring the blurred one, new SVD features are extracted based on re-blurred singular value difference (RESVD) of the corresponding local gradient patches. Then, perceptual weight based on Just Noticeable Blur (JNB) is introduced to guide the sparse blur map estimation obtained with the SVD features. Finally, the full defocus blur map is constructed from the sparse defocus blur map by the Matting Laplace algorithm. Visual evaluations are conducted on the CUHK, DUT and CTCUG datasets, employing mean absolute error (MAE) and Fβ-measure as quantitative evaluation metrics. The experimental results demonstrate that the proposed defocus blur detection method is superior to 13 state-of-the-art methods. On the DUT dataset, the proposed method yields a high Fβ-measure (0.802) with a low MAE (0.081) compared to other methods (Fβ≤0.799, MAE ≥0.099). On the CUHK and CTCUG datasets, our method achieves the best balance between Fβ-measure and MAE. Furthermore, our method results in better visual effects than other methods regarding realism and quality.

Distill-DBDGAN: Knowledge Distillation and Adversarial Learning Framework for Defocus Blur Detection

Defocus blur detection (DBD) aims to segment the blurred regions from a given image affected by defocus blur. It is a crucial pre-processing step for various computer vision tasks. With the increasing popularity of small mobile devices, there is a need for a computationally efficient method to detect defocus blur accurately. We propose an efficient defocus blur detection method that estimates the probability of each pixel being focused or blurred in resource-constraint devices. Despite remarkable advances made by the recent deep learning-based methods, they still suffer from several challenges such as background clutter, scale sensitivity, indistinguishable low-contrast focused regions from out-of-focus blur, and especially high computational cost and memory requirement. To address the first three challenges, we develop a novel deep network that efficiently detects blur map from the input blurred image. Specifically, we integrate multi-scale features in the deep network to resolve the scale ambiguities and simultaneously modeled the non-local structural correlations in the high-level blur features. To handle the last two issues, we eventually frame our DBD algorithm to perform knowledge distillation by transferring information from the larger teacher network to a compact student network. All the networks are adversarially trained in an end-to-end manner to enforce higher order consistencies between the output and the target distributions. Experimental results demonstrate the state-of-the-art performance of the larger teacher network, while our proposed lightweight DBD model imitates the output of the teacher network without significant loss in accuracy. The codes, pre-trained model weights, and the results will be made publicly available.

Nonlinear kernel based feature maps for blur‐sensitive unsharp masking of JPEG images

In this paper, a method for estimating the blur regions of an image is first proposed, resorting to a mixture of linear and nonlinear convolutional kernels. The blur map obtained is then utilized to enhance images such that the enhancement strength is an inverse function of the amount of measured blur. The blur map can also be used for tasks such as attention-based object classification, low light image enhancement, and more. A CNN architecture is trained with nonlinear upsampling layers using a standard blur detection benchmark dataset, with the help of blur target maps. Further, it is proposed to use the same architecture to build maps of areas affected by the typical JPEG artifacts, ringing and blockiness. The blur map and the artifact map pair permit to build an activation map for the enhancement of a (possibly JPEG compressed) image. Extensive experiments on standard test images verify the quality of the maps obtained using the algorithm and their effectiveness in locally controlling the enhancement, for superior perceptual quality. Last but not least, the computation time for generating these maps is much lower than the one of other comparable algorithms.

Joint strong edge and multi-stream adaptive fusion network for non-uniform image deblurring

Non-uniform motion deblurring has been a challenging problem in the field of computer vision. Currently, deep learning-based deblurring methods have made promising achievements. In this paper, we propose a new joint strong edge and multi-stream adaptive fusion network to achieve non-uniform motion deblurring. The edge map and the blurred map are jointly used as network inputs and Edge Extraction Network (EEN) guides the Deblurring Network (DN) for image recovery and to complement the important edge information. The Multi-stream Adaptive Fusion Module (MAFM) adaptively fuses the edge information and features from the encoder and decoder to reduce feature redundancy to avoid image artifacts. Furthermore, the Dense Attention Feature Extraction Module (DAFEM) is designed to focus on the severely blurred regions of blurry images to obtain important recovery information. In addition, an edge loss function is added to measure the difference of edge features between the generated and clear images to further recover the edges of the deblurred images. Experiments show that our method outperforms currently public methods in terms of PSNR, SSIM and VIF, and generates images with less blur and sharper edges.

Improving defocus blur measures using robust regularization

This paper presents a robust regularization scheme for the enhancement of initial blur map in single image blur detection. The proposed technique considers the structural differences between guidance and initial blur map. Specifically, we propose to improve the initial blur map by optimizing a nonconvex energy function that jointly leverages structural information from guidance and blur map. Nonconvex problem is solved by majorize-minimize algorithm and an improved blur map is obtained that is adequately smooth and has better edge-preserving properties. Ultimately, this results in high-quality blur segmentation.

Online Projector Deblurring Using a Convolutional Neural Network.

Projector deblurring is an important technology for dynamic projection mapping (PM), where the distance between a projector and a projection surface changes in time. However, conventional projector deblurring techniques do not support dynamic PM because they need to project calibration patterns to estimate the amount of defocus blur each time the surface moves. We present a deep neural network that can compensate for defocus blur in dynamic PM. The primary contribution of this paper is a unique network structure that consists of an extractor and a generator. The extractor explicitly estimates a defocus blur map and a luminance attenuation map. These maps are then injected into the middle layers of the generator network that computes the compensation image. We also propose a pseudo-projection technique for synthesizing physically plausible training data, considering the geometric misregistration that potentially happens in actual PM systems. We conducted simulation and actual PM experiments and confirmed that: (1) the proposed network structure is more suitable than a simple, more general structure for projector deblurring; (2) the network trained with the proposed pseudo-projection technique can compensate projection images for defocus blur artifacts in dynamic PM; and (3) the network supports the translation speed of the surface movement within a certain range that covers normal human motions.

Defocus Blur Segmentation Using Genetic Programming and Adaptive Threshold

Detection and classification of the blurred and the non-blurred regions in images is a challenging task due to the limited available information about blur type, scenarios and level of blurriness. In this paper, we propose an effective method for blur detection and segmentation based on transfer learning concept. The proposed method consists of two separate steps. In the first step, genetic programming (GP) model is developed that quantify the amount of blur for each pixel in the image. The GP model method uses the multi-resolution features of the image and it provides an improved blur map. In the second phase, the blur map is segmented into blurred and non-blurred regions by using an adaptive threshold. A model based on support vector machine (SVM) is developed to compute adaptive threshold for the input blur map. The performance of the proposed method is evaluated using two different datasets and compared with various state-of-the-art methods. The comparative analysis reveals that the proposed method performs better against the state-of-the-art techniques.

Deep Multi-Scale Feature Learning for Defocus Blur Estimation

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). Then, we estimate the defocus blur amount at pattern edges only, and explore an interpolation 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. Both tasks (edge classification and blur estimation) are performed by deep convolutional neural networks (CNNs) that share weights to learn meaningful local features from multi-scale patches centered at edge locations. Experiments on naturally defocused images show that the proposed method presents qualitative and quantitative results that outperform state-of-the-art (SOTA) methods, with a good compromise between running time and accuracy.

A Noise-Robust Partial Blur Detection Algorithm Combining Walsh Transform and Sequency Truncation

Blur detection is an important yet challenging task in computer vision. The previous algorithms are mostly difficult to achieve a cost-benefit balance and their performance is largely limited when faced with the blur image polluted by noise. To address these issues, a fast and unsupervised blur detection algorithm is proposed, which is robust to noise. First, a re-blur strategy and Walsh transform are utilized to analyze the input image in sequency domain. Meanwhile, the low-sequency zone of sequency spectrum is adaptively truncated to eliminate the noise interference. Then, a noise-robust local blur metric is constructed and pixel-wise blurriness is calculated to obtain the blur map. Finally, the blur region is segmented using the clustering-guided multi-scale growth framework. Experimental results on CUHK and DUT datasets demonstrate that the proposed algorithm can detect the image blur effectively and efficiently, and achieves the state-of-the-art performance on multiple indicators like precision, recall, <italic>F</italic>₁-measure, mean absolute error, and mean runtime. Especially on noise-polluted conditions, the proposed algorithm significantly surpasses other competitive algorithms.

Noise-immune image blur detection via sequency spectrum truncation

Blur detection is aimed to differentiate the blurry and sharp regions from a given image. This task has attracted much attention in recent years due to its importance in computer vision with the integration of image processing and artificial intelligence. However, blur detection still suffers from problems such as the oversensitivity to image noise and the difficulty in cost–benefit balance. To deal with these issues, we propose an accurate and efficient blur detection method, which is concise in architecture and robust against noise. First, we develop a sequency spectrum-based blur metric to estimate the blurriness of each pixel by integrating a re-blur scheme and the Walsh transform. Meanwhile, to eliminate the noise interference, we propose an adaptive sequency spectrum truncation strategy by which we can obtain an accurate blur map even in noise-polluted cases. Finally, a multi-scale fusion segmentation framework is designed to extract the blur region based on the clustering-guided region growth. Experimental results on benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance and the best balance between cost and benefit. It offers an average F1 score of 0.887, MAE of 0.101, detecting time of 0.7 s, and training time of 0.5 s. Especially for noise-polluted blurry images, the proposed method achieves the F1 score of 0.887 and MAE of 0.101, which significantly surpasses other competitive approaches. Our method yields a cost–benefit advantage and noise immunity that has great application prospect in complex sensing environment.

A new image-quality evaluating and enhancing methodology for bridge inspection using an unmanned aerial vehicle

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).

Hierarchical Residual Embedding Network for Real-Time De-focus Blur Detection

Defocus blur detection, as an important pre-processing step of image processing, has attracted more and more attention. Albeit great success has been made, there are still several challenges for accurate defocus blur detection, such as the interference of background clutter, sensitivity to scales, missing boundary details and large computational burden. For handling these issues, we present a deep neural network which hierarchically embeds residual learning blocks for defocus blur detection. Based on the feature pyramid structure, we extract deep features with varying scales via utilizing a backbone fully convolutional network and generate a coarse score map by using the last layer of feature maps. Then we design a hierarchical residual embedding module to fuse different levels of features in a layer-wise manner. By embedding different layer-wise features in the top-down pathway, coarse-level semantic information from the deep layers can be seamlessly propagated to shallow layers, while fine details in the shallow layers can be used to refine the boundary between out-of-focus and in-focus regions. For each layer, a side output is generated by using a residual learning block. For capturing multi-scale information, the multiple side outputs of different layers are fed into a designed fusion block for yielding the final blur map result. Experimental results on two commonly used datasets show that our proposed network can more accurately locate the defocus blur regions with sharpened details being well preserved when compared to other previous state-of-the-arts. In addition, our approach is fast as well and can run at a speed of more than 25 FPS when processing an image with size 427 x 640.

Quality Assessment and Identification of Fingerprints by Analysis of the Image’s Structural Properties

The paper addresses the problem of assessing the quality of fingerprint images using spatial analysis methods. The author proposes using the previously developed mathematical model to describe the set of magnitudes of the image gradient. The model is based on the two-parameter Weibull distribution. The author proposes two approaches to assess the quality of fingerprints. The first approach is implemented by the so-called “Full reference method”, which compares the Weibull distribution parameters’ values of statistical estimates. The results of identifying sweat pores using this method are presented. The second approach is called the “No-Reference method” and is used to assess fingerprints’ quality when analyzing and identifying the information content of their individual sections. It is proposed to use an image blur map as a quality characteristic and a statistical estimate of the Weibull distribution shape parameter as a measure of the blur. The shape parameter is estimated at each image point by the combination of magnitudes of the image gradient in the vicinity of the point; in this, the previously developed blur mapping technique is applied. The specific examples illustrate effectiveness of the proposed approaches.

BR$^2$Net: Defocus Blur Detection Via a Bidirectional Channel Attention Residual Refining Network

Due to the remarkable potential applications, defocus blur detection, which aims to separate blurry regions from an image, has attracted much attention. Although significant progress has been made by many methods, there are still various challenges that hinder the results, e.g., confusing background areas, sensitivity to the scale and missing the boundary details of the defocus blur regions. To solve these issues, in this paper, we propose a deep convolutional neural network (CNN) for defocus blur detection via a Bi-directional Residual Refining network (BR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net). Specifically, a residual learning and refining module (RLRM) is designed to correct the prediction errors in the intermediate defocus blur map. Then, we develop a bidirectional residual feature refining network with two branches by embedding multiple RLRMs into it for recurrently combining and refining the residual features. One branch of the network refines the residual features from the shallow layers to the deep layers, and the other branch refines the residual features from the deep layers to the shallow layers. In such a manner, both the low-level spatial details and highlevel semantic information can be encoded step by step in two directions to suppress background clutter and enhance the detected region details. The outputs of the two branches are fused to generate the final results. In addition, with the observation that different feature channels have different extents of discrimination for detecting blurred regions, we add a channel attention module to each feature extraction layer to select more discriminative features for residual learning. To promote further research on defocus blur detection, we create a new dataset with various challenging images and manually annotate their corresponding pixelwise ground truths. The proposed network is validated on two commonly used defocus blur detection datasets and our newly collected dataset by comparing it with 10 other state-of-the-art methods. Extensive experiments with ablation studies demonstrate that BR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net consistently and significantly outperforms the competitors in terms of both the efficiency and accuracy.

Single image defocus estimation by modified gaussian function

AbstractThis article presents an algorithm to estimate the defocus blur from a single image. Most of the existing methods estimate the defocus blur at edge locations, which further involves the reblurring process. For this purpose, existing methods use the traditional Gaussian function in the phase of reblurring but it is found that the traditional Gaussian kernel is sensitive to the edges and can cause loss of edges information. Hence, there are more chances of missing spatially varying blur at edge locations. We offer the repeated averaging filters as an alternative to the traditional Gaussian function, which is more effective, and estimate the spatially varying defocus blur at edge locations. By using repeated averaging filters, a blur sparse map is computed. The obtained sparse map is propagated by integration of superpixels segmentation and transductive inference to estimate full defocus blur map. Our adopted method of repeated averaging filters has less computational time of defocus blur map estimation and has better visual estimates of the final defocus recovered map. Moreover, it has surpassed many previous state‐of‐the‐art proposed systems in terms of quantative analysis.

Defocus blur detection based on multiscale SVD fusion in gradient domain

Recently, defocus blur detection has been an extensive study, but it is still full of challenges in the blur estimation without having any prior knowledge of test image such as blur kernel, degree, or camera parameters. Inspired by the observation that the degree of defocus blur depth could be distinguished by different frequencies, a novel blur metric based on Multiscale SVD fusion (M-SVD) is proposed. The blur metric fuses different sub-bands of the selected singular values (SVs) in multiscale image windows, which could drastically reduce the chances of false positives for blur detection and overcome the difficulty that the sharp region is misjudged for a blur region because of its smooth texture. Finally, a blur map is applied on the test image combined with post-processing operation meanshift cluster to segment the blur region. Experimental results demonstrate that the proposed method can detect the defocus blur regions of test images with a satisfactory performance and outperforms the state-of-the-art methods.