Query: Image enhancement

title: An Underwater Image Enhancement Benchmark Dataset and Beyond
abstract: Underwater image enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real world images. It is thus unclear how these algorithms would perform on images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater image enhancement using large scale real world images. In this paper, we construct an Underwater Image Enhancement Benchmark (UIEB) including 950 real world underwater images, 890 of which have the corresponding reference images. We treat the rest 60 underwater images which cannot obtain satisfactory reference images as challenging data. Using this dataset, we conduct a comprehensive study of the state of the art underwater image enhancement algorithms qualitatively and quantitatively. In addition, we propose an underwater image enhancement network (called Water Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs) The benchmark evaluations and the proposed Water Net demonstrate the performance and limitations of state of the art algorithms, which shed light on future research in underwater image enhancement. The dataset and code are available at https:/li chongyi.github.io/proj_benchmark.html.

title: Zero Reference Deep Curve Estimation for Low Light Image Enhancement
abstract: The paper presents a novel method, Zero Reference Deep Curve Estimation (Zero DCE) which formulates light enhancement as a task of image specific curve estimation with a deep network. Our method trains a lightweight deep network, DCE Net, to estimate pixel wise and high order curves for dynamic range adjustment of a given image. The curve estimation is specially designed, considering pixel value range, monotonicity, and differentiability. Zero DCE is appealing in its relaxed assumption on reference images, i.e. it does not require any paired or unpaired data during training. This is achieved through a set of carefully formulated non reference loss functions, which implicitly measure the enhancement quality and drive the learning of the network. Our method is efficient as image enhancement can be achieved by an intuitive and simple nonlinear curve mapping. Despite its simplicity, we show that it generalizes well to diverse lighting conditions. Extensive experiments on various benchmarks demonstrate the advantages of our method over state of the art methods qualitatively and quantitatively. Furthermore, the potential benefits of our Zero DCE to face detection in the dark are discussed.

title: Underwater Image Enhancement Using a Multiscale Dense Generative Adversarial Network
abstract: Underwater image enhancement has received much attention in underwater vision research. However, raw underwater images easily suffer from color distortion, underexposure, and fuzz caused by the underwater scene. To address the above mentioned problems, we propose a new multiscale dense generative adversarial network (GAN) for enhancing underwater images. The residual multiscale dense block is presented in the generator, where the multiscale, dense concatenation, and residual learning can boost the performance, render more details, and utilize previous features, respectively. And the discriminator employs computationally light spectral normalization to stabilize the training of the discriminator. Meanwhile, nonsaturating GAN loss function combining $L_1$ loss and gradient loss is presented to focus on image features of ground truth. Final enhanced results on synthetic and real underwater images demonstrate the superiority of the proposed method, which outperforms nondeep and deep learning methods in both qualitative and quantitative evaluations. Furthermore, we perform an ablation study to show the contributions of each component and carry out application tests to further demonstrate the effectiveness of the proposed method.

title: Fast Underwater Image Enhancement for Improved Visual Perception
abstract: In this letter, we present a conditional generative adversarial network based model for real time underwater image enhancement. To supervise the adversarial training, we formulate an objective function that evaluates the perceptual image quality based on its global content, color, local texture, and style information. We also present EUVP, a large scale dataset of a paired and an unpaired collection of underwater images (of 'poor' and 'good' quality) that are captured using seven different cameras over various visibility conditions during oceanic explorations and human robot collaborative experiments. In addition, we perform several qualitative and quantitative evaluations which suggest that the proposed model can learn to enhance underwater image quality from both paired and unpaired training. More importantly, the enhanced images provide improved performances of standard models for underwater object detection, human pose estimation, and saliency prediction. These results validate that it is suitable for real time preprocessing in the autonomy pipeline by visually guided underwater robots. The model and associated training pipelines are available at https:/github.com/xahidbuffon/funie gan.

title: From Fidelity to Perceptual Quality: A Semi Supervised Approach for Low Light Image Enhancement
abstract: Under exposure introduces a series of visual degradation, i.e. decreased visibility, intensive noise, and biased color, etc. To address these problems, we propose a novel semi supervised learning approach for low light image enhancement. A deep recursive band network (DRBN) is proposed to recover a linear band representation of an enhanced normal light image with paired low/normal light images, and then obtain an improved one by recomposing the given bands via another learnable linear transformation based on a perceptual quality driven adversarial learning with unpaired data. The architecture is powerful and flexible to have the merit of training with both paired and unpaired data. On one hand, the proposed network is well designed to extract a series of coarse to fine band representations, whose estimations are mutually beneficial in a recursive process. On the other hand, the extracted band representation of the enhanced image in the first stage of DRBN (recursive band learning) bridges the gap between the restoration knowledge of paired data and the perceptual quality preference to real high quality images. Its second stage (band recomposition) learns to recompose the band representation towards fitting perceptual properties of high quality images via adversarial learning. With the help of this two stage design, our approach generates enhanced results with well reconstructed details and visually promising contrast and color distributions. Qualitative and quantitative evaluations demonstrate the superiority of our DRBN.

title: Diving Deeper into Underwater Image Enhancement: A Survey
abstract: The powerful representation capacity of deep learning has made it inevitable for the underwater image enhancement community to employ its potential. The exploration of deep underwater image enhancement networks is increasing over time, and hence; a comprehensive survey is the need of the hour. In this paper, our main aim is two fold, 1) to provide a comprehensive and in depth survey of the deep learning based underwater image enhancement, which covers various perspectives ranging from algorithms to open issues, and 2) to conduct a qualitative and quantitative comparison of the deep algorithms on diverse datasets to serve as a benchmark, which has been barely explored before. To be specific, we first introduce the underwater image formation models, which are the base of training data synthesis and design of deep networks, and also helpful for understanding the process of underwater image degradation. Then, we review deep underwater image enhancement algorithms, and a glimpse of some of the aspects of the current networks is presented including network architecture, network parameters, training data, loss function, and training configurations. We also summarize the evaluation metrics and underwater image datasets. Following that, a systematically experimental comparison is carried out to analyze the robustness and effectiveness of deep algorithms. Meanwhile, we point out the shortcomings of current benchmark datasets and evaluation metrics. Finally, we discuss several unsolved open issues and suggest possible research directions. We hope that all efforts done in this paper might serve as a comprehensive reference for future research and call for the development of deep learning based underwater image enhancement.

title: Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories
abstract: An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, encompassing both biotic and abiotic variables, and describing the ecological dynamics of the observed species. In this context, imaging devices are valuable tools that complement other biological and oceanographic monitoring devices. Nevertheless, large amounts of images or movies cannot all be manually processed, and autonomous routines for recognizing the relevant content, classification, and tagging are urgently needed. In this work, we propose a pipeline for the analysis of visual data that integrates video/image annotation tools for defining, training, and validation of datasets with video/image enhancement and machine and deep learning approaches. Such a pipeline is required to achieve good performance in the recognition and classification tasks of mobile and sessile megafauna, in order to obtain integrated information on spatial distribution and temporal dynamics. A prototype implementation of the analysis pipeline is provided in the context of deep sea videos taken by one of the fixed cameras at the LoVe Ocean Observatory network of Lofoten Islands (Norway) at 260 m depth, in the Barents Sea, which has shown good classification results on an independent test dataset with an accuracy value of 76.18% and an area under the curve (AUC) value of 87.59%

title: LIME: Low Light Image Enhancement via Illumination Map Estimation
abstract: When one captures images in low light conditions, the images often suffer from low visibility. Besides degrading the visual aesthetics of images, this poor quality may also significantly degenerate the performance of many computer vision and multimedia algorithms that are primarily designed for high quality inputs. In this paper, we propose a simple yet effective low light image enhancement (LIME) method. More concretely, the illumination of each pixel is first estimated individually by finding the maximum value in R, G, and B channels. Furthermore, we refine the initial illumination map by imposing a structure prior on it, as the final illumination map. Having the well constructed illumination map, the enhancement can be achieved accordingly. Experiments on a number of challenging low light images are present to reveal the efficacy of our LIME and show its superiority over several state of the arts in terms of enhancement quality and efficiency.

title: An Experiment Based Review of Low Light Image Enhancement Methods
abstract: Images captured under poor illumination conditions often exhibit characteristics such as low brightness, low contrast, a narrow gray range, and color distortion, as well as considerable noise, which seriously affect the subjective visual effect on human eyes and greatly limit the performance of various machine vision systems. The role of low light image enhancement is to improve the visual effect of such images for the benefit of subsequent processing. This paper reviews the main techniques of low light image enhancement developed over the past decades. First, we present a new classification of these algorithms, dividing them into seven categories: gray transformation methods, histogram equalization methods, Retinex methods, frequency domain methods, image fusion methods, defogging model methods and machine learning methods. Then, all the categories of methods, including subcategories, are introduced in accordance with their principles and characteristics. In addition, various quality evaluation methods for enhanced images are detailed, and comparisons of different algorithms are discussed. Finally, the current research progress is summarized, and future research directions are suggested.

title: Novel infrared image enhancement optimization algorithm combined with DFOCS
abstract: In the paper, the issue about infrared image enhancement is addressed. First a discrete type fractional order chaotic system (DFOCS) is constructed. Then a kind of chaotic mapping based on the new system is utilized to design an optimization algorithm for the achievement of infrared image enhancement. In the final, some typical experiments are conducted to display the advantage and effectiveness of the given optimization algorithm, which is much suitable for the potential application in fault detection.