Kernel learning for blind image recovery from motion blur

Qin, Fuqiang; Fang, Shuai; Wang, Lifang; Yuan, Xiaohui; Elhoseny, Mohamed; Yuan, Xiaojing

doi:10.1007/s11042-020-09012-3

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…Additionally, this approach assumes that just the blurring process alters the sparsity of the dark channel and that noise may have an impact on an image's dark pixels [13]. As discussed in [14] the magnitude of image structure was significantly reduced by motion blur led to imprecise kernel estimation. In [15] suggests that extending the 𝐿0regularization to non-uniform image deblurring with a better non-blind deconvolution approach.…”

Section: Related Workmentioning

confidence: 99%

Single Image Integrated Deblurring Algorithm in Non-Uniform Environment

Rechard Lee,

Celine Wong

2024

ARASET

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Motion blurriness in an image caused by camera shake during exposure is unavoidable. It could lead to information loss and degradation in the image quality. Therefore, many researchers are dedicated to developing image deblurring techniques to recover clear images from blurred images. During the deblurring process, structural edges in images play a vital part in estimating the blur kernels. For images with rich textures, fine-scale edges become more apparent. This will cause vagueness in the image’s structural edges and affect the accuracy of the kernel estimation process. In this study, we propose a single-image motion deblurring by kernel estimation method combined with L0-Regularized Intensity and Gradient Prior, and enhanced Scale Aware Smoothing methods. Two types of non-uniform datasets are used, which are real and synthetic. While synthetic datasets are utilized to assess the consistency in performance across real and synthetic images, real datasets are used to portray the level of detail and variation of actual blurred images. The dataset is divided into five categories (people, nature, manmade, text, and night light). Two image quality metrics were selected: full-reference assessment, including learned perceptual image patch similarity (LPIPS), peak signal-to-noise ratio (PSNR), and structural similarity indexing method (SSIM) for synthetic datasets, and no-reference assessments, including blind/reference-less image spatial quality (BRISQUE), natural image quality evaluator (NIQE) and perception-based image quality evaluator (PIQE) for real datasets. According to the findings, the fusion method performs best in the text category, followed by manmade and in nature, then night light and poor in people. The proposed method not only removes fine-scale edges and preserves the boundary sharpness, but also improved the estimated blur kernel.

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Section: Related Workmentioning

confidence: 99%

Single Image Integrated Deblurring Algorithm in Non-Uniform Environment

Rechard Lee,

Celine Wong

2024

ARASET

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“…If the TX end sends a data packet to the WiFi AP which is in the dormant period-but at the same time, there are other receiving nodes in the wake-up period but cannot transmit data-the data packet will still be in a waiting state until the corresponding device wakes up to complete the data packet transmission. is will cause data transmission delays, channel congestion in local equipment, and imbalance channel utilization [25][26][27].…”

Section: Coexisting and Communicationmentioning

confidence: 99%

An Asynchronous Data Transmission Method in Heterogeneous Wireless Networks

Chang-hao

et al. 2020

Complexity

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With the massive deployment and application of intelligent systems such as smart life, smart medical care, and smart transportation, it is becoming more common for heterogeneous wireless networks such as WiFi, Zigbee, and Bluetooth to coexist in the same physical space. To improve the communication efficiency under the coexistence of heterogeneous wireless networks, this paper proposes an asynchronous data transmission method based on CTC technology, which constantly monitors the sleep and wake-up cycles of heterogeneous devices, makes full use of the wake-up cycle of the receiving terminal, and uses asynchronous methods to achieve cross-technology transmission of data packets. We also give a specific implementation scheme and use the program to simulate the transmission effect. The results show that our proposed method effectively reduces data transmission time and balances channel transmission throughput.

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