6 research outputs found
Review on Image Inpainting using Intelligence Mining Techniques
Objective Inpainting is a technique for fixing or removing undesired areas of an image. Methods In present scenario, image plays a vital role in every aspect such as business images, satellite images, and medical images and so on. Results and Conclusion This paper presents a comprehensive review of past traditional image inpainting methods and the present state-of-the-art deep learning methods and also detailed the strengths and weaknesses of each to provide new insights in the field
Deep image prior inpainting of ancient frescoes in the Mediterranean Alpine arc
The unprecedented success of image reconstruction approaches based on deep
neural networks has revolutionised both the processing and the analysis
paradigms in several applied disciplines. In the field of digital humanities,
the task of digital reconstruction of ancient frescoes is particularly
challenging due to the scarce amount of available training data caused by
ageing, wear, tear and retouching over time. To overcome these difficulties, we
consider the Deep Image Prior (DIP) inpainting approach which computes
appropriate reconstructions by relying on the progressive updating of an
untrained convolutional neural network so as to match the reliable piece of
information in the image at hand while promoting regularisation elsewhere. In
comparison with state-of-the-art approaches (based on variational/PDEs and
patch-based methods), DIP-based inpainting reduces artefacts and better adapts
to contextual/non-local information, thus providing a valuable and effective
tool for art historians. As a case study, we apply such approach to reconstruct
missing image contents in a dataset of highly damaged digital images of
medieval paintings located into several chapels in the Mediterranean Alpine Arc
and provide a detailed description on how visible and invisible (e.g.,
infrared) information can be integrated for identifying and reconstructing
damaged image regions.Comment: 26 page
Deep Image Prior Amplitude SAR Image Anonymization
This paper presents an extensive evaluation of the Deep Image Prior (DIP) technique for image inpainting on Synthetic Aperture Radar (SAR) images. SAR images are gaining popularity in various applications, but there may be a need to conceal certain regions of them. Image inpainting provides a solution for this. However, not all inpainting techniques are designed to work on SAR images. Some are intended for use on photographs, while others have to be specifically trained on top of a huge set of images. In this work, we evaluate the performance of the DIP technique that is capable of addressing these challenges: it can adapt to the image under analysis including SAR imagery; it does not require any training. Our results demonstrate that the DIP method achieves great performance in terms of objective and semantic metrics. This indicates that the DIP method is a promising approach for inpainting SAR images, and can provide high-quality results that meet the requirements of various applications
Lightweight Modules for Efficient Deep Learning based Image Restoration
Low level image restoration is an integral component of modern artificial
intelligence (AI) driven camera pipelines. Most of these frameworks are based
on deep neural networks which present a massive computational overhead on
resource constrained platform like a mobile phone. In this paper, we propose
several lightweight low-level modules which can be used to create a
computationally low cost variant of a given baseline model. Recent works for
efficient neural networks design have mainly focused on classification.
However, low-level image processing falls under the image-to-image' translation
genre which requires some additional computational modules not present in
classification. This paper seeks to bridge this gap by designing generic
efficient modules which can replace essential components used in contemporary
deep learning based image restoration networks. We also present and analyse our
results highlighting the drawbacks of applying depthwise separable
convolutional kernel (a popular method for efficient classification network)
for sub-pixel convolution based upsampling (a popular upsampling strategy for
low-level vision applications). This shows that concepts from domain of
classification cannot always be seamlessly integrated into image-to-image
translation tasks. We extensively validate our findings on three popular tasks
of image inpainting, denoising and super-resolution. Our results show that
proposed networks consistently output visually similar reconstructions compared
to full capacity baselines with significant reduction of parameters, memory
footprint and execution speeds on contemporary mobile devices.Comment: Accepted at: IEEE Transactions on Circuits and Systems for Video
Technology (Early Access Print) | |Codes Available at:
https://github.com/avisekiit/TCSVT-LightWeight-CNNs | Supplementary Document
at:
https://drive.google.com/file/d/1BQhkh33Sen-d0qOrjq5h8ahw2VCUIVLg/view?usp=sharin