A Hybrid Motion Deblurring Strategy Using Patch Based Edge Restoration and Bilateral Filter

Chang, Chia-Feng; Wu, Jiunn-Lin; Chen, Kuan‐Jen

doi:10.1007/s10851-018-0797-x

Cited by 7 publications

(6 citation statements)

References 20 publications

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“…For humans, the quality-dropped images are not a feast for the eyes, but humans still can analyze their contents, due to experimental knowledge, such as imagination, reasoning ability [23]- [25], etc. However, for machines or computers, all knowedge of the model are learned from the very training dataset [26]. In this case, the interferences of the training data are severe for the performance of the models.…”

Section: Related Workmentioning

confidence: 99%

Multitask Learning Mechanism for Remote Sensing Image Motion Deblurring

Fang

Cao

Wang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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As a fundamental preprocessing technique, remote sensing image motion deblurring is important for visual understanding tasks. Most conventional approaches formulate the image motion deblurring task as a kernel estimation. Because the kernel estimation is a highly ill-posed problem, many priors have been applied to model the images and kernels. Even though these methods have obtained relatively better performances, they are usually timeconsuming and not robust for different conditions. To address this problem, we propose a multitask learning mechanism for remote sensing image motion deblurring in this article, which contains an image restoration subtask and an image texture complexity recognition one. First, we consider the image motion deblurring problem as a domain transformation problem, from the blurred domain to a clear one. Specifically, the blurred domain represents the data space consisted of blurring images, and the definition of clear domain is similar. Second, we design a novel weighted attention map loss to enhance the reconstruction capability of the restoration subbranch for difficult local regions. Third, based on the restoration subbranch, a recognition subbranch is incorporated into the framework to guide the deblurring process, which provides the auxiliary texture complexity information to help the optimization of restoration subbranch. Additionally, in order to optimize the proposed network, we construct three large-scale datasets, and each sample in the dataset contains a clear image, a blurred image, and its texture label obtained by corresponding texture complexity. Finally, the experimental results on three constructed datasets demonstrate the robustness and the effectiveness of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Multitask Learning Mechanism for Remote Sensing Image Motion Deblurring

Fang

Cao

Wang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In recent years, research on image sharpening has been growing actively due to its existing challenges, including the over-sharpening effect, amplification of latent noise, overshoot and others. Among the existing sharpening algorithms, various popular techniques are Laplacian filtering, unsharp masking (UM) [10], shock filtering and local neighbourhood operators [15] [26]. Zohair proposed a nimble filter, a single kernel to obtain specific spatial information from the input image and deduct it from the original image with certain weighting parameters to control the level of sharpness improved [15].…”

Section: Related Workmentioning

confidence: 99%

“…A hybrid deblurring strategy introduced by Chang et al tends to process strong edges and weak edges separately. The author applied a patch-based method to sharpen the existing strong edges while using bilateral filtering to remove the noise and smooth edges [26]. Canny edge detection provides double threshold values, high and low thresholds, which are critically important to filter out the weak edges while retaining the strong edges.…”

Section: • Approximation Of Edges Is More Accuratementioning

confidence: 99%

Mini Kirsch Edge Detection and Its Sharpening Effect

Sia¹,

Swee²,

Yahya³

et al. 2021

IJEEI

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In computer vision, edge detection is a crucial step in identifying the objects' boundaries in an image. The existing edge detection methods function in either spatial domain or frequency domain, fail to outline the high continuity boundaries of the objects. In this work, we modified four-directional mini Kirsch edge detection kernels which enable full directional edge detection. We also introduced the novel involvement of the proposed method in image sharpening by adding the resulting edge map onto the original input image to enhance the edge details in the image. From the edge detection performance tests, our proposed method acquired the highest true edge pixels and true nonedge pixels detection, yielding the highest accuracy among all the comparing methods. Moreover, the sharpening effect offered by our proposed framework could achieve a more favorable visual appearance with a competitive score of peak signal-to-noise ratio and structural similarity index value compared to the most widely used unsharp masking and Laplacian of Gaussian sharpening methods. The edges of the sharpened image are further enhanced could potentially contribute to better boundary tracking and higher segmentation accuracy.

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“…Many of them focused on designing different data terms [4,24] and various kinds of image priors [3,5,7,[25][26][27][28]. In addition, patch-based methods [13,29,30] have been developed to sidestep classical regularizers and had shown impressive performance. These methods usually searched for similar patches [13] or exploited sharp patches in an external dictionary [29,30], which both required heavy computation.…”

Section: Related Workmentioning

confidence: 99%

Blind Deconvolution with Scale Ambiguity

Fan

Wang

et al. 2020

Applied Sciences

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Recent years have witnessed significant advances in single image deblurring due to the increasing popularity of electronic imaging equipment. Most existing blind image deblurring algorithms focus on designing distinctive image priors for blur kernel estimation, which usually play regularization roles in deconvolution formulation. However, little research effort has been devoted to the relative scale ambiguity between the latent image and the blur kernel. The well-known L 1 normalization constraint, i.e., fixing the sum of all the kernel weights to be one, is commonly selected to remove this ambiguity. In contrast to this arbitrary choice, we in this paper introduce the L p -norm normalization constraint on the blur kernel associated with a hyper-Laplacian prior. We show that the employed hyper-Laplacian regularizer can be transformed into a joint regularized prior based on a scale factor. We quantitatively show that the proper choice of p makes the joint prior sufficient to favor the sharp solutions over the trivial solutions (the blurred input and the delta kernel). This facilitates the kernel estimation within the conventional maximum a posterior (MAP) framework. We carry out numerical experiments on several synthesized datasets and find that the proposed method with p = 2 generates the highest average kernel similarity, the highest average PSNR and the lowest average error ratio. Based on these numerical results, we set p = 2 in our experiments. The evaluation on some real blurred images demonstrate that the results by the proposed methods are visually better than the state-of-the-art deblurring methods.

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A Hybrid Motion Deblurring Strategy Using Patch Based Edge Restoration and Bilateral Filter

Cited by 7 publications

References 20 publications

Multitask Learning Mechanism for Remote Sensing Image Motion Deblurring

Multitask Learning Mechanism for Remote Sensing Image Motion Deblurring

Mini Kirsch Edge Detection and Its Sharpening Effect

Blind Deconvolution with Scale Ambiguity

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