About this Research Topic

Manuscript Submission Deadline 25 October 2022

Signal Inpainting aims to infer missing or corrupted signals from partially known information. It is an inverse problem, widely studied in Signal Processing, for image, audio, and video restoration.

As an example, in the domain of Audio Signal Processing, the quality of audio signals that have been corrupted by impulsive interference, clipping, and missing frames can effectively be improved and restored. In real-time audio transmission, however, algorithms with high time complexity can be limited in their practical applications, and computationally simple and fast algorithms are often desired. Reducing the complexity of the algorithm while maintaining the inpainting performance still remains, however, a highly challenging task.

In the domain of Image Processing, images can be corrupted by background noise and missing pixels, and inpainting techniques have a variety of applications in the fields of satellite remote sensing, biomedicine, and cultural relics restoration. However, there are still many challenges in image inpainting as it is difficult to recover irregular texture features and details, and it is also a great challenge to reconstruct a large missing area in an image. Furthermore, recovered images may also have blocking artifacts.

Apart from the above applications, Signal Inpainting methods could also be used in other fields, such as video restoration, and for dealing with radar echoes, and cleaning data from radio telescopes.

This Research Topic aims at collecting recent contributions from scientists, researchers, engineers, and practitioners working in the field of Signal Inpainting and restoration. This platform will allow the exchange of ideas on algorithm development, theoretical analysis, and practical applications, and will stimulate the development of novel algorithms, theories, tools, and dataset for the study of Signal Inpainting.

Themes of interest in this Research Topic include but are not limited to the following:

• Signal Inpainting models (linear or nonlinear)
• Signal Inpainting theories
• Signal Inpainting algorithms
• Signal Inpainting performance analysis
• Links between Signal Inpainting and other signal restoration problems (e.g. denoising, declipping, deconvolution, deblurring, etc.)
• Applications in audio, speech, image, video, biomedicine, and other areas
• Software tools, datasets, systems or hardware
• Tutorial or review on Signal Inpainting

Keywords: Signal Inpainting, Inpainting Models, Inpainting Theories, Inpainting Algorithms, Image Inpainting, Audio Inpainting, Video Inpainting, Restoring, Repairing


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Signal Inpainting aims to infer missing or corrupted signals from partially known information. It is an inverse problem, widely studied in Signal Processing, for image, audio, and video restoration.

As an example, in the domain of Audio Signal Processing, the quality of audio signals that have been corrupted by impulsive interference, clipping, and missing frames can effectively be improved and restored. In real-time audio transmission, however, algorithms with high time complexity can be limited in their practical applications, and computationally simple and fast algorithms are often desired. Reducing the complexity of the algorithm while maintaining the inpainting performance still remains, however, a highly challenging task.

In the domain of Image Processing, images can be corrupted by background noise and missing pixels, and inpainting techniques have a variety of applications in the fields of satellite remote sensing, biomedicine, and cultural relics restoration. However, there are still many challenges in image inpainting as it is difficult to recover irregular texture features and details, and it is also a great challenge to reconstruct a large missing area in an image. Furthermore, recovered images may also have blocking artifacts.

Apart from the above applications, Signal Inpainting methods could also be used in other fields, such as video restoration, and for dealing with radar echoes, and cleaning data from radio telescopes.

This Research Topic aims at collecting recent contributions from scientists, researchers, engineers, and practitioners working in the field of Signal Inpainting and restoration. This platform will allow the exchange of ideas on algorithm development, theoretical analysis, and practical applications, and will stimulate the development of novel algorithms, theories, tools, and dataset for the study of Signal Inpainting.

Themes of interest in this Research Topic include but are not limited to the following:

• Signal Inpainting models (linear or nonlinear)
• Signal Inpainting theories
• Signal Inpainting algorithms
• Signal Inpainting performance analysis
• Links between Signal Inpainting and other signal restoration problems (e.g. denoising, declipping, deconvolution, deblurring, etc.)
• Applications in audio, speech, image, video, biomedicine, and other areas
• Software tools, datasets, systems or hardware
• Tutorial or review on Signal Inpainting

Keywords: Signal Inpainting, Inpainting Models, Inpainting Theories, Inpainting Algorithms, Image Inpainting, Audio Inpainting, Video Inpainting, Restoring, Repairing


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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