A Hybrid Sparse Representation Model for Image Restoration

Zhou, Caiyue; Kong, Yanfen; Zhang, Chuanyong; Sun, Li; Wu, Dongmei; Zhou, Chongbo

doi:10.3390/s22020537

Cited by 2 publications

(2 citation statements)

References 43 publications

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“…However, the exemplar-based method requires more time to repair when the damage size of the target image is large. The third is the sparse representation-based image inpainting algorithm [7] [8]. However, this method is based on the assumption of image sparsity.…”

Section: Related Workmentioning

confidence: 99%

Mural image shedding diseases inpainting algorithm based on structure priority

Pen

Wang²,

Zhang³

2023

Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)

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The painted murals in Mogao Grottoes and Longmen Grottoes are symbols of China history and culture. However, most of the murals with complex texture and structure have suffered from different degrees of disease erosion after thousands of years. It is necessary to restore the damaged parts of the murals and to accurately restore their contents. In recent years, the use of new virtual technologies such as digital images to repair the damage can largely avoid secondary damage to the murals caused by manual restoration methods. Therefore, this paper takes the restoration of the most typical shedding diseases to the Mogao Caves murals in Dunhuang as an example. Furthermore, the research object of this paper is the shedding diseases including contour lines. For the traditional virtual methods of repairing shedding diseases, the structure and texture are usually restored at the same time, and these methods have little effect on the accurate removal of shedding disease through the contour line. It can be seen that shedding disease through the contour line is more difficult to repair, and more appropriate inpainting methods need to be explored. Considering the particularity of the shedding disease that passes through the contour line, this paper proposes a mural image inpainting algorithm based on structure priority to repair the shedding diseases. First, the structure repair problem is further converted into a optimization problem, and then the global optimization capability of the genetic algorithm is used to realize the connection of the structure information of the damaged area. Then, the texture is filled by subarea optimization to obtain an ideal repair effect, which can reasonably and effectively solve the problem of shedding disease repair through the contour line. Subjective and objective evaluation of experimental results is also better than other comparative methods.

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Section: Related Workmentioning

confidence: 99%

Mural image shedding diseases inpainting algorithm based on structure priority

Pen

Wang²,

Zhang³

2023

Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)

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“…Sparse approximation ideas became a widespread practice in the processing of signals and images in different fields of science [11][12][13][14][15][16]. The sparse approximation works for nonstationary signal analysis better than classical processing methods such as Short Time Fourier Transform and wavelet transform [17].…”

Section: Adaptive Matching Pursuitmentioning

confidence: 99%

Adaptive Approach to Time-Frequency Analysis of AE Signals of Rocks

Lukovenkova¹,

Marapulets²,

Solodchuk³

2022

Sensors

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The paper describes a new adaptive approach to the investigation of acoustic emission of rocks, the anomalies of which may serve as short-term precursors of strong earthquakes. The basis of the approach is complex methods for monitoring acoustic emission and for analysis of its time-frequency content. Piezoceramic hydrophones and vector receivers, installed at the bottom of natural and artificial water bodies, as well as in boreholes with water, are used as acoustic emission sensors. To perform a time-frequency analysis of geoacoustic signals, we use a sparse approximation based on the developed Adaptive Matching Pursuit algorithm. The application of this algorithm in the analysis makes it possible to adapt to the concrete characteristics of each geoacoustic pulse. Results of the application of the developed approach for the investigation of acoustic emission anomalies, occurring before earthquakes, are presented. We analyzed the earthquakes, that occurred from 2011 to 2016 in the seismically active region of the Kamchatka peninsula, which is a part of the circum-Pacific orogenic belt also known as the “Ring of Fire”. It was discovered that geoacoustic pulse frequency content changes before a seismic event and returns to the initial state after an earthquake. That allows us to make a conclusion on the transformation of acoustic emission source scales before earthquakes. The obtained results may be useful for the development of the systems for environmental monitoring and detection of earthquake occurrences.

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A Hybrid Sparse Representation Model for Image Restoration

Cited by 2 publications

References 43 publications

Mural image shedding diseases inpainting algorithm based on structure priority

Mural image shedding diseases inpainting algorithm based on structure priority

Adaptive Approach to Time-Frequency Analysis of AE Signals of Rocks

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