Are Classification Deep Neural Networks Good for Blind Image ...

Feasible because the framework of adversarial sample provides a way to create watermarked image of good quality. Relevant because the DL network provides a ...

This paper investigates a new family of transformation based on Deep Neural Networks trained with supervision for a classification task.

Are Deep Neural Networks good for blind image watermarking?

This paper investigates a new family of transformation based on Deep Learning networks and shows that this approach is feasible as it yields a good quality ...

Are Deep Neural Networks good for blind image watermarking?

Deep Learning (DL) has completely revolutionized the field of Computer Vision. It started with image classifi- cation [14] and is now spreading ...

Are Deep Neural Networks good for blind image watermarking?

... In watermarking, the use of convolutional neural network (CNN)-based approaches instead of transformed-based approaches is extremely beneficial due to their ...

Are Deep Neural Networks good for blind image watermarking? - Inria

This paper investigates a new family of transformation based on Deep Learning networks. Motivations come from the Computer Vision literature.

Are Classification Deep Neural Networks Good for Blind Image ...

Are Classification Deep Neural Networks Good for Blind Image Watermarking? Vukotić, V.; Chappelier, V.; Furon, T. Entropy 22(2). 2020. ISSN/ISBN: 1099-4300.

Deep Neural Networks for Blind Image Quality Assessment - arXiv

This casts great challenges to deep neural network (DNN) based blind IQA (BIQA), which requires large-scale training data that is representative ...

Deep learning network for blind image quality assessment

The recent advance of deep neural network (DNN) can help to solve this problem, since the DNN is found to better capture the essential attributes of images. We ...

Blind Image Quality Assessment Model Based on Deep ...

This paper proposes a novel blind IQA model evolved from VGG-16[1], a state-of-the-art classification deep convolutional neural network(CNN). It modifies the ...

Blind Image Blur Estimation via Deep Learning (Blur Classification ...

A learning-based method using a pre-trained deep neural network (DNN) and a general regression neural network (GRNN) is proposed to first ...

Are Deep Neural Networks Adequate Behavioral Models of Human ...

... good models of human visual perception. In this article, we review evidence regarding current DNNs as adequate behavioral models of human ...

On the Use of Deep Learning for Blind Image Quality Assessment

Experimental results on the LIVE In the Wild Image Quality Challenge Database and on the LIVE Image Quality Assessment Database show that ...

Deep neural networks for automated damage classification in image ...

Convolutional neural networks (CNNs) are popular deep learning frameworks that have revolutionized computer vision and led to advancements in ...

End-to-End Blind Image Quality Prediction with Cascaded Deep ...

Abstract—The deep convolutional neural network (CNN) has achieved great success in image recognition. Many image quality assessment (IQA) methods directly ...

Which neural network is best for image classification? - Quora

Convolutional Neural Networks are the best choices for image classification (usually). They could outperform almost all other methods in ...

Deep neural networks and image classification in biological vision

Despite the putative similarity of these networks to some properties of the biological vision system, and the remarkable levels of performance accuracy of some ...

[PDF] On the use of deep learning for blind image quality assessment

The best proposal, named DeepBIQ, estimates the image quality by average-pooling the scores predicted on multiple subregions of the original image, ...

End-to-End Blind Image Quality Assessment Using Deep Neural ...

How larger images are handled will be explained in. Section III-C. MEON consists of two subtasks accomplished by two sub-networks. Sub-network I aims to ...

Are Neural Networks only really useful for Image Classification?

Random forest would probably do fine enough. KNN maybe not as well if survey questions have discrete data - continuous data may also be fine ...