Automatic Extraction of Blur Regions on a Single Image Based on Semantic Segmentation

Shen, Aodong; Han, Dong; Wang, Kun; Kong, Yaguang; Wu, Jiasong; Hu, Shu

doi:10.1109/access.2020.2978084

Cited by 5 publications

(5 citation statements)

References 44 publications

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“…The edge detection algorithm explained in [ 30 , 31 ] detects the object’s artifacts and also performs the analysis of the object’s contouring to determine the defocused-blur pixels. On the contrary, the region-wise segmentation algorithm is required for object detection in images of natural scenes [ 29 , 32 , 33 , 34 , 35 ] by exploring a region along with high-frequency techniques. For instance, the latest works [ 36 , 37 , 38 ] apply a higher-order statistics (HOS) measure from out-of-focused scenes to explore the in-focused regions.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Defocus Blur Segmentation Scheme Based on Hybrid LTP and PCNN

Basar

Waheed

Ali

et al. 2022

Sensors

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The defocus or motion effect in images is one of the main reasons for the blurry regions in digital images. It can affect the image artifacts up to some extent. However, there is a need for automatic defocus segmentation to separate blurred and sharp regions to extract the information about defocus-blur objects in some specific areas, for example, scene enhancement and object detection or recognition in defocus-blur images. The existence of defocus-blur segmentation algorithms is less prominent in noise and also costly for designing metric maps of local clarity. In this research, the authors propose a novel and robust defocus-blur segmentation scheme consisting of a Local Ternary Pattern (LTP) measured alongside Pulse Coupled Neural Network (PCNN) technique. The proposed scheme segments the blur region from blurred fragments in the image scene to resolve the limitations mentioned above of the existing defocus segmentation methods. It is noticed that the extracted fusion of upper and lower patterns of proposed sharpness-measure yields more noticeable results in terms of regions and edges compared to referenced algorithms. Besides, the suggested parameters in the proposed descriptor can be flexible to modify for performing numerous settings. To test the proposed scheme’s effectiveness, it is experimentally compared with eight referenced techniques along with a defocus-blur dataset of 1000 semi blurred images of numerous categories. The model adopted various evaluation metrics comprised of Precision, recall, and F1-Score, which improved the efficiency and accuracy of the proposed scheme. Moreover, the proposed scheme used some other flavors of evaluation parameters, e.g., Accuracy, Matthews Correlation-Coefficient (MCC), Dice-Similarity-Coefficient (DSC), and Specificity for ensuring provable evaluation results. Furthermore, the fuzzy-logic-based ranking approach of Evaluation Based on Distance from Average Solution (EDAS) module is also observed in the promising integrity analysis of the defocus blur segmentation and also in minimizing the time complexity.

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Section: Literature Reviewmentioning

confidence: 99%

An Efficient Defocus Blur Segmentation Scheme Based on Hybrid LTP and PCNN

Basar

Waheed

Ali

et al. 2022

Sensors

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“…In [19], [20], an edge-based detection method is used to detect the artificial object and analyze the object's contour by measuring the defocused pixels. Conversely, the regionbased segmentation is essential in detecting objects in natural images [21]- [24], [79] that explores a region by using highfrequency mechanisms. For example, recent works [25]- [27] use a higher-order statistics (HOS) map from defocused images to highlight the focused regions.…”

Section: A Defocused Image Segmentation and Pcnn Algorithmmentioning

confidence: 99%

A Novel Defocused Image Segmentation Method Based on PCNN and LBP

et al. 2021

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The defocus blur concept adds an artistic effect and enables an enhancement in the visualization of image scenery. Moreover, some specialized computer vision fields, such as object recognition or scene restoration enhancement, might need to perform segmentation to separate the blurred and nonblurred regions in partially blurred images. This study proposes a sharpness measure comprised of a Local Binary Pattern (LBP) descriptor and Pulse Coupled Neural Network (PCNN) component used to implement a robust approach for segmenting in-focus regions from out of focus sections in the scene. The proposed approach is very robust in the sense that the parameters of the model can be modified to accommodate different settings. The presented metric exploits the fact that, in general, local patches of the image in blurry regions have less prominent LBP descriptors than non-blurry regions. The proposed approach combines this sharpness measure with the PCNN algorithm; the images are segmented along with clear regions and edges of segmented objects. The proposed approach has been tested on a dataset comprised of 1000 defocused images with eight state-of-the-art methods. Based on a set of evaluation metrics, i.e., precision, recall, and F1-Measure, the results show that the proposed algorithm outperforms previous works in terms of prominent accuracy and efficiency improvement. The proposed approach also uses other evaluation parameters, i.e., Accuracy, Matthews Correlation Coefficient (MCC), Dice Similarity Coefficient (DSC), and Specificity, for providing a better assessment of the results obtained by our proposal. Moreover, we adopted a fuzzy logic ranking scheme inspired by the Evaluation Based on Distance from Average Solution (EDAS) technique to interpret the defocus segmentation integrity. The experimental outputs illustrate that the proposed approach outperforms the referenced methods by optimizing the segmentation quality and reducing the computational complexity.

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“…Im upp and Im low are the upper and lower patterns of equivalent decimal numbers from the extracted two-level binary patterns from equation (5). After that, we computed the deviation of the neighboring pixels again with decimal values of the upper and lower patterns computed from equation (6).…”

Section: Multisequential Deviated Patterns (Msdps)mentioning

confidence: 99%

“…For multi-image detection, the knowledge of the blur densities, type, sensor information of the cameras, and other additional information is required [4]. In contrast, a single image can be split into sharp and blur regions without having any prior information about the blur and the device used to capture that image [5,6]. Moreover, the existing approaches presented for DBD can be categorized into frequency-based [7][8][9][10][11][12][13], depth-based [14][15][16][17], or local sharpness metric map-based for segmentation of blur and nonblur regions [6,[18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

A Robust Approach for Blur and Sharp Regions’ Detection Using Multisequential Deviated Patterns

Khan

Javed

Irtaza

et al. 2021

International Journal of Optics

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Blur detection (BD) is an important and challenging task in digital imaging and computer vision applications. Accurate segmentation of homogenous smooth and blur regions, low-contrast focal regions, missing patches, and background clutter, without having any prior information about the blur, are the fundamental challenges of BD. Previous work on BD has emphasized much effort on designing local sharpness metric maps from the images. However, the smooth/blurred regions having the same patterns as sharp regions make them problematic. This paper presents a robust novel method to extract the local metric map for blurred and nonblurred regions based on multisequential deviated patterns (MSDPs). Unlike the preceding, MSDP extracts the local sharpness metric map on the images at multiple scales using different adaptive thresholds to overcome the problems of smooth/blur regions and missing patches. By using the integral values of the image along with image masking and Otsu thresholding, highly accurate segmented regions of the images are acquired. We argue/hypothesize that the local sharpness map extraction by using direct integral information of the image is highly affected by the threshold selected for distinction between the regions, whereas MSDP feature extraction overcomes the limitations substantially by using automatic threshold computation over multiple scales of the images. Moreover, the proposed method extracts the relatively accurate sharp regions from the high-dense blur and noisy images. Experiments are conducted on two commonly used SHI and DUT datasets for blur and sharp region classifications. The results indicate the effectiveness of the proposed method in terms of sharp segmented regions. Experimental results of qualitative and quantitative comparisons of the proposed method with ten comparative methods demonstrate the superiority of our method. Moreover, the proposed method is also computationally efficient over state-of-the-art methods.

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Automatic Extraction of Blur Regions on a Single Image Based on Semantic Segmentation

Cited by 5 publications

References 44 publications

An Efficient Defocus Blur Segmentation Scheme Based on Hybrid LTP and PCNN

An Efficient Defocus Blur Segmentation Scheme Based on Hybrid LTP and PCNN

A Novel Defocused Image Segmentation Method Based on PCNN and LBP

A Robust Approach for Blur and Sharp Regions’ Detection Using Multisequential Deviated Patterns

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