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In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels. If the real age estimation research spans over decades, the study of apparent age estimation or the age as perceived by other humans from a face image is a recent endeavor. We tackle both tasks with our convolutional neural networks (CNNs) of VGG-16 architecture which are pre-trained on ImageNet for image classification. We pose the age estimation problem as a deep classification problem followed by a softmax expected value refinement. The key factors of our solution are: deep learned models from large data, robust face alignment, and expected value formulation for age regression. We validate our methods on standard benchmarks and achieve state-of-the-art results for both real and apparent age estimation. [ABSTRACT FROM AUTHOR]

Deformable Parts Models (DPM) are the current state-of-the-art for object detection. Nevertheless they seem sub-optimal in the representation of deformations. Object deformations are often continuous and not confined to big parts. Therefore we propose to replace the DPM star model based on big parts by a deformation field. This consists of a grid of small parts connected with pairwise constraints which can better handle continuous deformations. The naive application of this model for object detection would consist of a bounded sliding window approach: for each possible location of the image the best part configuration within a limited bound around this location is found. This is computationally very expensive.Instead, we propose a different inference procedure, where an iterative image-level search finds the best object hypothesis. We show that this approach is faster than bounded sliding windows yet produces comparable accuracy. Experiments further show that the deformation field can better approximate real object deformations and therefore, for certain classes, produces even better detection accuracy than state-of-the-art DPM. Finally, the same approach is adapted to model-free tracking, showing improved accuracy also in this case. [ABSTRACT FROM AUTHOR]

Representing data as a linear combination of a set of selected known samples is of interest for various machine learning applications such as dimensionality reduction or classification. k -Nearest Neighbors ( k NN) and its variants are still among the best-known and most often used techniques. Some popular richer representations are Sparse Representation (SR) based on solving an l 1 -regularized least squares formulation, Collaborative Representation (CR) based on l 2 -regularized least squares, and Locally Linear Embedding (LLE) based on an l 1 -constrained least squares problem. We propose a novel sparse representation, the Iterative Nearest Neighbors (INN). It combines the power of SR and LLE with the computational simplicity of k NN. We empirically validate our representation in terms of sparse support signal recovery and compare with similar Matching Pursuit (MP) and Orthogonal Matching Pursuit (OMP), two other iterative methods. We also test our method in terms of dimensionality reduction and classification, using standard benchmarks for faces (AR), traffic signs (GTSRB), and objects (PASCAL VOC 2007). INN compares favorably to NN, MP, and OMP, and on par with CR and SR, while being orders of magnitude faster than the latter. On the downside, INN does not scale well with higher dimensionalities of the data. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose the Weighted Collaborative Representation based Classifier (WCRC) and its variants. [•] Weighting independently of the query keeps the complexity and improves over CRC. [•] Weighting adaptive to the query can improve the performance, but increases the computation. [•] We investigate kernel extensions. [•] We validate on faces, handwritten digits, and traffic signs datasets. [Copyright &y& Elsevier]

Several applications require information about street furniture. Part of the task is to survey all traffic signs. This has to be done for millions of km of road, and the exercise needs to be repeated every so often. We used a van with eight roof-mounted cameras to drive through the streets and took images every meter. The paper proposes a pipeline for the efficient detection and recognition of traffic signs from such images. The task is challenging, as illumination conditions change regularly, occlusions are frequent, sign positions and orientations vary substantially, and the actual signs are far less similar among equal types than one might expect. We combine 2D and 3D techniques to improve results beyond the state-of-the-art, which is still very much preoccupied with single view analysis. For the initial detection in single frames, we use a set of colour- and shape-based criteria. They yield a set of candidate sign patterns. The selection of such candidates allows for a significant speed up over a sliding window approach while keeping similar performance. A speedup is also achieved through a proposed efficient bounded evaluation of AdaBoost detectors. The 2D detections in multiple views are subsequently combined to generate 3D hypotheses. A Minimum Description Length formulation yields the set of 3D traffic signs that best explains the 2D detections. The paper comes with a publicly available database, with more than 13,000 traffic signs annotations. [ABSTRACT FROM AUTHOR]

• In this paper, we propose a variation of deep residual convolutional neural networks with robustness and efficiency in both learning and testing. • More importantly, we propose multiple strategies for model adaptation to the internal contents of the lowresolution input image and analyze their strong points and weaknesses. • Our adaptation especially favors images with repetitive structures or high resolutions. • We hope to arouse the interests of communities in focusing on internal priors, which are limited but have been proved effective and highly relevant. In recent years, impressive advances have been made in single-image super-resolution. Deep learning is behind much of this success. Deep(er) architecture design and external prior modeling are the key ingredients. The internal contents of the low-resolution input image are neglected with deep modeling, despite earlier works that show the power of using such internal priors. In this paper, we propose a variation of deep residual convolutional neural networks, which has been carefully designed for robustness and efficiency in both learning and testing. Moreover, we propose multiple strategies for model adaptation to the internal contents of the low-resolution input image and analyze their strong points and weaknesses. By trading runtime and using internal priors, we achieve improvements from 0.1 to 0.3 dB PSNR over the reported results on standard datasets. Our adaptation especially favors images with repetitive structures or high resolutions. It indicates a more practical usage when our adaption approach applies to sequences or videos in which adjacent frames are strongly correlated in their contents. Moreover, the approach can be combined with other simple techniques, such as back-projection and enhanced prediction, to realize further improvements. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal IET Computer Vision, discussing the topic of advanced image restoration and enhancement. The editorial highlights the challenges faced in this field, such as the complexity of degradation models for real-world low-quality images and the difficulty of acquiring paired data. It also introduces a special issue of the journal that includes five accepted papers, which focus on video reconstruction and image super-resolution. The editorial concludes by providing brief summaries of each accepted paper. The guest editors of the special issue are also mentioned, along with their research interests and affiliations. [Extracted from the article]

Video enhancement is a challenging problem, more than that of stills, mainly due to high computational cost, larger data volumes and the difficulty of achieving consistency in the spatio-temporal domain. In practice, these challenges are often coupled with the lack of example pairs, which inhibits the application of supervised learning strategies. To address these challenges, we propose an efficient adversarial video enhancement framework that learns directly from unpaired video examples. In particular, our framework introduces new recurrent cells that consist of interleaved local and global modules for implicit integration of spatial and temporal information. The proposed design allows our recurrent cells to efficiently propagate spatio-temporal information across frames and reduces the need for high complexity networks. Our setting enables learning from unpaired videos in a cyclic adversarial manner, where the proposed recurrent units are employed in all architectures. Efficient training is accomplished by introducing one single discriminator that learns the joint distribution of source and target domain simultaneously. The enhancement results demonstrate clear superiority of the proposed video enhancer over the state-of-the-art methods, in all terms of visual quality, quantitative metrics, and inference speed. Notably, our video enhancer is capable of enhancing over 35 frames per second of FullHD video (1080x1920). [ABSTRACT FROM AUTHOR]

Objectives: This review aims to share the current developments of artificial intelligence (AI) solutions in the field of medico-dental diagnostics of the face. The primary focus of this review is to present the applicability of artificial neural networks (ANN) to interpret medical images, together with the associated opportunities, obstacles, and ethico-legal concerns. Material and methods: Narrative literature review. Results: Narrative literature review. Conclusion: Curated facial images are widely available and easily accessible and are as such particularly suitable big data for ANN training. New AI solutions have the potential to change contemporary dentistry by optimizing existing processes and enriching dental care with the introduction of new tools for assessment or treatment planning. The analyses of health-related big data may also contribute to revolutionize personalized medicine through the detection of previously unknown associations. In regard to facial images, advances in medico-dental AI-based diagnostics include software solutions for the detection and classification of pathologies, for rating attractiveness and for the prediction of age or gender. In order for an ANN to be suitable for medical diagnostics of the face, the arising challenges regarding computation and management of the software are discussed, with special emphasis on the use of non-medical big data for ANN training. The legal and ethical ramifications of feeding patients' facial images to a neural network for diagnostic purposes are related to patient consent, data privacy, data security, liability, and intellectual property. Current ethico-legal regulation practices seem incapable of addressing all concerns and ensuring accountability. Clinical significance: While this review confirms the many benefits derived from AI solutions used for the diagnosis of medical images, it highlights the evident lack of regulatory oversight, the urgent need to establish licensing protocols, and the imperative to investigate the moral quality of new norms set with the implementation of AI applications in medico-dental diagnostics. [ABSTRACT FROM AUTHOR]

Recent works on plug-and-play image restoration have shown that a denoiser can implicitly serve as the image prior for model-based methods to solve many inverse problems. Such a property induces considerable advantages for plug-and-play image restoration (e.g., integrating the flexibility of model-based method and effectiveness of learning-based methods) when the denoiser is discriminatively learned via deep convolutional neural network (CNN) with large modeling capacity. However, while deeper and larger CNN models are rapidly gaining popularity, existing plug-and-play image restoration hinders its performance due to the lack of suitable denoiser prior. In order to push the limits of plug-and-play image restoration, we set up a benchmark deep denoiser prior by training a highly flexible and effective CNN denoiser. We then plug the deep denoiser prior as a modular part into a half quadratic splitting based iterative algorithm to solve various image restoration problems. We, meanwhile, provide a thorough analysis of parameter setting, intermediate results and empirical convergence to better understand the working mechanism. Experimental results on three representative image restoration tasks, including deblurring, super-resolution and demosaicing, demonstrate that the proposed plug-and-play image restoration with deep denoiser prior not only significantly outperforms other state-of-the-art model-based methods but also achieves competitive or even superior performance against state-of-the-art learning-based methods. The source code is available at https://github.com/cszn/DPIR. [ABSTRACT FROM AUTHOR]

Super-resolution (SR) is an essential class of low-level vision tasks, which aims to improve the resolution of images or videos in computer vision. In recent years, significant progress has been made in image and video super-resolution techniques based on deep learning. Nevertheless, most of the methods only consider SR with a few integer scale factors, which limits the application of the SR techniques to real-world problems. Recently, the methods to achieve arbitrary-scale super-resolution via a single model have attracted much attention. However, there is no work to thoroughly analyze the arbitrary-scale methods based on deep learning. In this work, we present a comprehensive and systematic review of 45 existing deep learning-based methods for arbitrary-scale image and video SR. We first classify the existing SR methods according to the resolved scale factors. Furthermore, we propose an in-depth taxonomy for state-of-the-art methods based on the core problem of how to achieve arbitrary-scale super-resolution, i.e., how to perform arbitrary-scale upsampling. Moreover, the performance of existing arbitrary-scale SR methods is compared, and their advantages and limitations are analyzed. We also provide some guidance for the selection of these methods in different real-world applications. Finally, we briefly discuss the future directions of arbitrary-scale super-resolution, which shows some inspirations for the progress of subsequent works on arbitrary-scale image and video super-resolution tasks. The repository of this work is available at https://github.com/Weepingchestnut/Arbitrary-Scale-SR. • This work is the first systematic review on arbitrary scale super-resolution (SR). • Two novel taxonomies for arbitrary scale SR methods are proposed. • The advantages and limitations of each class of methods are analyzed. • The evaluation on the performance of 24 algorithms are shown. • The trends and future directions are discussed. [ABSTRACT FROM AUTHOR]

This guest editorial summarizes the Special Section on Perceptually Optimized Imaging. [ABSTRACT FROM AUTHOR]