The vast majority of photos taken today are by mobile phones. While their quality is rapidly growing, due to physical limitations and cost constraints, mobile phone cameras struggle to compare in quality with DSLR cameras. This motivates us to computationally enhance these images. We extend upon the results of , where they are able to translate images from compact mobile cameras into images with comparable quality to high-resolution photos taken by DSLR cameras. However, the neural models employed require large amounts of computational resources and are not lightweight enough to run on mobile devices. We build upon the prior work and explore different network architectures targeting an increase in image quality and speed. With an efficient network architecture which does most of its processing in a lower spatial resolution, we achieve a significantly higher mean opinion score (MOS) than the baseline while speeding up the computation by 6.3 times on a consumer-grade CPU. This suggests a promising direction for neural-network-based photo enhancement using the phone hardware of the future.
aim to remove optical distortions from photos that have been taken out of focus, while the camera was moving, or of faraway geographical or astronomical features. The neural models employed are CNNs, typically trained on images with artificially added blur or haze, using a MSE loss function @cite_26 @cite_23 @cite_5 @cite_14 @cite_9 . Recently, datasets with both hazy and haze-free images were introduced @cite_4 and solutions such as the one of Ki @cite_6 were proposed, which use a GAN, in addition to L1 and perceptual losses. Similar techniques are effective for as well @cite_27 @cite_18 @cite_19 @cite_10 .
@cite_18 @cite_14 @cite_26 @cite_4 @cite_9 @cite_6 @cite_19 @cite_27 @cite_23 @cite_5 @cite_10
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This paper proposes a computationally efficient approach to detecting objects natively in 3D point clouds using convolutional neural networks (CNNs). In particular, this is achieved by leveraging a feature-centric voting scheme to implement novel convolutional layers which explicitly exploit the sparsity encountered in the input. To this end, we examine the trade-off between accuracy and speed for different architectures and additionally propose to use an L 1 penalty on the filter activations to further encourage sparsity in the intermediate representations. To the best of our knowledge, this is the first work to propose sparse convolutional layers and L 1 regularisation for efficient large-scale processing of 3D data. We demonstrate the efficacy of our approach on the KITTI object detection benchmark and show that VoteSDeep models with as few as three layers outperform the previous state of the art in both laser and laser-vision based approaches by margins of up to 40 while remaining highly competitive in terms of processing time. We demonstrate an imaging technique that allows identification and classification of objects hidden behind scattering media and is invariant to changes in calibration parameters within a training range. Traditional techniques to image through scattering solve an inverse problem and are limited by the need to tune a forward model with multiple calibration parameters (like camera field of view, illumination position etc.). Instead of tuning a forward model and directly inverting the optical scattering, we use a data driven approach and leverage convolutional neural networks (CNN) to learn a model that is invariant to calibration parameters variations within the training range and nearly invariant beyond that. This effectively allows robust imaging through scattering conditions that is not sensitive to calibration. The CNN is trained with a large synthetic dataset generated with a Monte Carlo (MC) model that contains random realizations of major calibration parameters. The method is evaluated with a time-resolved camera and multiple experimental results are provided including pose estimation of a mannequin hidden behind a paper sheet with 23 correct classifications out of 30 tests in three poses (76.6 accuracy on real-world measurements). This approach paves the way towards real-time practical non line of sight (NLOS) imaging applications. With deep learning becoming the dominant approach in computer vision, the use of representations extracted from Convolutional Neural Nets (CNNs) is quickly gaining ground on Fisher Vectors (FVs) as favoured state-of-the-art global image descriptors for image instance retrieval. While the good performance of CNNs for image classification are unambiguously recognised, which of the two has the upper hand in the image retrieval context is not entirely clear yet. In this work, we propose a comprehensive study that systematically evaluates FVs and CNNs for image retrieval. The first part compares the performances of FVs and CNNs on multiple publicly available data sets. We investigate a number of details specific to each method. For FVs, we compare sparse descriptors based on interest point detectors with dense single-scale and multi-scale variants. For CNNs, we focus on understanding the impact of depth, architecture and training data on retrieval results. Our study shows that no descriptor is systematically better than the other and that performance gains can usually be obtained by using both types together. The second part of the study focuses on the impact of geometrical transformations such as rotations and scale changes. FVs based on interest point detectors are intrinsically resilient to such transformations while CNNs do not have a built-in mechanism to ensure such invariance. We show that performance of CNNs can quickly degrade in presence of rotations while they are far less affected by changes in scale. We then propose a number of ways to incorporate the required invariances in the CNN pipeline. Overall, our work is intended as a reference guide offering practically useful and simply implementable guidelines to anyone looking for state-of-the-art global descriptors best suited to their specific image instance retrieval problem. Convolutional neural networks (CNNs) have been successfully applied to many recognition and learning tasks using a universal recipe; training a deep model on a very large dataset of supervised examples. However, this approach is rather restrictive in practice since collecting a large set of labeled images is very expensive. One way to ease this problem is coming up with smart ways for choosing images to be labelled from a very large collection (ie. active learning). Our empirical study suggests that many of the active learning heuristics in the literature are not effective when applied to CNNs in batch setting. Inspired by these limitations, we define the problem of active learning as core-set selection, ie. choosing set of points such that a model learned over the selected subset is competitive for the remaining data points. We further present a theoretical result characterizing the performance of any selected subset using the geometry of the datapoints. As an active learning algorithm, we choose the subset which is expected to yield best result according to our characterization. Our experiments show that the proposed method significantly outperforms existing approaches in image classification experiments by a large margin.