Illuminant estimation plays a key role in digital camera pipeline system, it aims at reducing color casting effect due to the influence of non-white illuminant. Recent researches handle this task by using Convolution Neural Network (CNN) as a mapping function from input image to a single illumination vector. However, global mapping approaches are difficult to deal with scenes under multi-light-sources. In this paper, we proposed a self-adaptive single and multi-illuminant estimation framework, which includes the following novelties: (1) Learning local self-adaptive kernels from the entire image for illuminant estimation with encoder-decoder CNN structure; (2) Providing confidence measurement for the prediction; (3) Clustering-based iterative fitting for computing single and multi-illumination vectors. The proposed global-to-local aggregation is able to predict multi-illuminant regionally by utilizing global information instead of training in patches, as well as brings significant improvement for single illuminant estimation. We outperform the state-of-the-art methods on standard benchmarks with the largest relative improvement of 16%. In addition, we collect a dataset contains over 13k images for illuminant estimation and evaluation. The code and dataset is available on https://github.com/LiamLYJ/KPF_WB
University of Milano Bicocca（米兰比可卡大学）；University of Pavia（帕维亚大学）
In this paper we present a method for the estimation of the color of the illuminant in RAW images. The method includes a Convolutional Neural Network that has been specially designed to produce multiple local estimates. A multiple illuminant detector determines whether or not the local outputs of the network must be aggregated into a single estimate. We evaluated our method on standard datasets with single and multiple illuminants, obtaining lower estimation errors with respect to those obtained by other general purpose methods in the state of the art.
In this paper we present a deep learning method to estimate the illuminant of an image. Our model is not trained with illuminant annotations, but with the objective of improving performance on an auxiliary task such as object recognition. To the best of our knowledge, this is the first example of a deep learning architecture for illuminant estimation that is trained without ground truth illuminants. We evaluate our solution on standard datasets for color constancy, and compare it with state of the art methods. Our proposal is shown to outperform most deep learning methods in a cross-dataset ev…
We present Convolutional Mean (CM) - a simple and fast convolutional neural network for illuminant estimation. Our proposed method only requires a small neural network model (1.1K parameters) and a 48 x 32 thumbnail input image. Our unoptimized Python implementation takes 1 ms/image, which is arguably 3-3750x faster than the current leading solutions with similar accuracy. Using two public datasets, we show that our proposed light-weight method offers accuracy comparable to the current leading methods' (which consist of thousands/millions of parameters) across several measures.
Reliable estimation of illuminant chromaticity is crucial for simulating color constancy and for white balancing digital images. However, estimating illuminant chromaticity from a single image is an ill-posed task, in general, and existing solutions typically employ a variety of assumptions and heuristics. In this paper, we present a new, physically-based, approach for estimating illuminant chromaticity from interreflections of light between diffuse surfaces. Our approach assumes that all of the direct illumination in the scene has the same chromaticity, and that at least two areas where inter…
Huawei Technologies（华为技术有限公司）；University of East Anglia（东英吉利大学）
The aim of colour constancy is to discount the effect of the scene illumination from the image colours and restore the colours of the objects as captured under a 'white' illuminant. For the majority of colour constancy methods, the first step is to estimate the scene illuminant colour. Generally, it is assumed that the illumination is uniform in the scene. However, real world scenes have multiple illuminants, like sunlight and spot lights all together in one scene. We present in this paper a simple yet very effective framework using a deep CNN-based method to estimate and use multiple illumina…
White balancing is a fundamental step in the image processing pipeline. The process involves estimating the chromaticity of the illuminant or light source and using the estimate to correct the image to remove any color cast. Given the importance of the problem, there has been much previous work on illuminant estimation. Recently, an approach based on ensembles of univariate regression trees that are fit using the squared-error loss function has been proposed and shown to give excellent performance. In this paper, we show that a simpler and more accurate ensemble model can be learned by (i) usi…
University of East Anglia（东英吉利大学）；AkzoNobel（阿克苏诺贝尔）；Amazon（亚马逊）；University of Milan-Bicocca（米兰比可卡大学）；Simon Fraser University（西蒙弗雷泽大学）；Samsung Semiconductor Inc（三星半导体公司）
In a previous work, it was shown that there is a curious problem with the benchmark ColorChecker dataset for illuminant estimation. To wit, this dataset has at least 3 different sets of ground-truths. Typically, for a single algorithm a single ground-truth is used. But then different algorithms, whose performance is measured with respect to different ground-truths, are compared against each other and then ranked. This makes no sense. We show in this paper that there are also errors in how each ground-truth set was calculated. As a result, all performance rankings based on the ColorChecker data…
University of Lyon（里昂大学）；UJM-Saint-Etienne（圣艾蒂安大学）；CNRS（法国国家科学研究中心）；Institut d’Optique Graduate School（法国高等光学研究所）；Laboratoire Hubert Curien（休伯特·居里安实验室）；Computer Vision Center（计算机视觉中心）；Universitat Autonoma de Barcelona（巴塞罗那自治大学）
In this work, we propose a CNN-based approach to estimate the spectral reflectance of a surface and the spectral power distribution of the light from a single RGB image of a V-shaped surface. Interreflections happening in a concave surface lead to gradients of RGB values over its area. These gradients carry a lot of information concerning the physical properties of the surface and the illuminant. Our network is trained with only simulated data constructed using a physics-based interreflection model. Coupling interreflection effects with deep learning helps to retrieve the spectral reflectance …
Auto white balance (AWB) is applied by camera hardware at capture time to remove the color cast caused by the scene illumination. The vast majority of white-balance algorithms assume a single light source illuminates the scene; however, real scenes often have mixed lighting conditions. This paper presents an effective AWB method to deal with such mixed-illuminant scenes. A unique departure from conventional AWB, our method does not require illuminant estimation, as is the case in traditional camera AWB modules. Instead, our method proposes to render the captured scene with a small set of prede…
Fudan University（复旦大学）；Northeastern State University（东北州立大学）；Nokia-Bell Labs（诺基亚贝尔实验室）
As a novel method eliminating chromatic aberration on objects, computational color constancy has becoming a fundamental prerequisite for many computer vision applications. Among algorithms performing this task, the learning-based ones have achieved great success in recent years. However, they fail to fully consider the spatial information of images, leaving plenty of room for improvement of the accuracy of illuminant estimation. In this paper, by exploiting the spatial information of images, we propose a color constancy algorithm called Attention Dense Color Constancy (ADCC) using convolutiona…
Hefei University of Technology（合肥工业大学）；Harbin Institute of Technology（哈尔滨工业大学）
Computational color constancy refers to the estimation of the scene illumination and makes the perceived color relatively stable under varying illumination. In the past few years, deep Convolutional Neural Networks (CNNs) have delivered superior performance in illuminant estimation. Several representative methods formulate it as a multi-label prediction problem by learning the local appearance of image patches using CNNs. However, these approaches inevitably make incorrect estimations for the ambiguous patches affected by their neighborhood contexts. Inaccurate local estimates are likely to br…
While modern deep neural networks (DNNs) achieve state-of-the-art results for illuminant estimation, it is currently necessary to train a separate DNN for each type of camera sensor. This means when a camera manufacturer uses a new sensor, it is necessary to retrain an existing DNN model with training images captured by the new sensor. This paper addresses this problem by introducing a novel sensor-independent illuminant estimation framework. Our method learns a sensor-independent working space that can be used to canonicalize the RGB values of any arbitrary camera sensor. Our learned space re…
Boosting Illuminant Estimation in Deep Color Constancy through Enhancing Brightness Robustness
School of Computer Science and Cyber Engineering, Guangzhou University（广州大学计算机科学与网络工程学院）；State Key Laboratory of Blockchain and Data Security, Zhejiang University（浙江大学区块链与数据安全国家重点实验室）；Information Security Research Group, University College London（伦敦大学学院信息安全研究组）
Indian Institute of Technology, Guwahati（印度理工学院古瓦哈提分校）；International Institute of Information Technology, Bangalore（班加罗尔国际信息技术研究所）
In this paper, a robust vehicle local position estimation with the help of single camera sensor and GPS is presented. A modified Inverse Perspective Mapping, illuminant Invariant techniques and object detection based approach is used to localize the vehicle in the road. Vehicles current lane, its position from road boundary and other cars are used to define its local position. For this purpose Lane markings are detected using a Laplacian edge feature, robust to shadowing. Effect of shadowing and extra sun light are removed using Lab color space and illuminant invariant techniques. Lanes are as…
Spectrum Aware Illumination Estimation Using Multispectral Image
Department of Electronic and Electrical Engineering, Ewha W. University（成均馆大学电子与电气工程系）；Telechips；Samsung Advanced Institute of Technology（三星先进技术研究所）；Department of Design, Ewha W. University（成均馆大学设计系）
Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Mila（米拉）；Technical University of Munich（慕尼黑工业大学）
Contemporary approaches frame the color constancy problem as learning camera specific illuminant mappings. While high accuracy can be achieved on camera specific data, these models depend on camera spectral sensitivity and typically exhibit poor generalisation to new devices. Additionally, regression methods produce point estimates that do not explicitly account for potential ambiguities among plausible illuminant solutions, due to the ill-posed nature of the problem. We propose a Bayesian framework that naturally handles color constancy ambiguity via a multi-hypothesis strategy. Firstly, we s…
University of British Columbia（不列颠哥伦比亚大学）；Huawei（华为）
Computational Colour Constancy (CCC) consists of estimating the colour of one or more illuminants in a scene and using them to remove unwanted chromatic distortions. Much research has focused on illuminant estimation for CCC on single images, with few attempts of leveraging the temporal information intrinsic in sequences of correlated images (e.g., the frames in a video), a task known as Temporal Colour Constancy (TCC). The state-of-the-art for TCC is TCCNet, a deep-learning architecture that uses a ConvLSTM for aggregating the encodings produced by CNN submodules for each image in a sequence.…
In this paper we present a framework for the design and implementation of offset equivariant networks, that is, neural networks that preserve in their output uniform increments in the input. In a suitable color space this kind of networks achieves equivariance with respect to the photometric transformations that characterize changes in the lighting conditions. We verified the framework on three different problems: image recognition, illuminant estimation, and image inpainting. Our experiments show that the performance of offset equivariant networks are comparable to those in the state of the a…
University of Electronic Science and Technology of China（电子科技大学）；Chinese Academy of Sciences（中国科学院）
It is an ill-posed problem to recover the true scene colors from a color biased image by discounting the effects of scene illuminant and camera spectral sensitivity (CSS) at the same time. Most color constancy (CC) models have been designed to first estimate the illuminant color, which is then removed from the color biased image to obtain an image taken under white light, without the explicit consideration of CSS effect on CC. This paper first studies the CSS effect on illuminant estimation arising in the inter-dataset-based CC (inter-CC), i.e., training a CC model on one dataset and then test…
There is widespread interest in estimating the fluorescence properties of natural materials in an image. However, the separation between reflected and fluoresced components is difficult, because it is impossible to distinguish reflected and fluoresced photons without controlling the illuminant spectrum. We show how to jointly estimate the reflectance and fluorescence from a single set of images acquired under multiple illuminants. We present a framework based on a linear approximation to the physical equations describing image formation in terms of surface spectral reflectance and fluorescence…
In this study, a novel illuminant color estimation framework is proposed for computational color constancy, which incorporates the high representational capacity of deep-learning-based models and the great interpretability of assumption-based models. The well-designed building block, feature map reweight unit (ReWU), helps to achieve comparative accuracy on benchmark datasets with respect to prior state-of-the-art deep learning based models while requiring more compact model size and cheaper computational cost. In addition to local color estimation, a confidence estimation branch is also inclu…
Style may refer to different concepts (e.g. painting style, hairstyle, texture, color, filter, etc.) depending on how the feature space is formed. In this work, we propose a novel idea of interpreting the lighting in the single- and multi-illuminant scenes as the concept of style. To verify this idea, we introduce an enhanced auto white-balance (AWB) method that models the lighting in single- and mixed-illuminant scenes as the style factor. Our AWB method does not require any illumination estimation step, yet contains a network learning to generate the weighting maps of the images with differe…
MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；Adobe Research（奥多比研究院）
Collections of images under a single, uncontrolled illumination have enabled the rapid advancement of core computer vision tasks like classification, detection, and segmentation. But even with modern learning techniques, many inverse problems involving lighting and material understanding remain too severely ill-posed to be solved with single-illumination datasets. To fill this gap, we introduce a new multi-illumination dataset of more than 1000 real scenes, each captured under 25 lighting conditions. We demonstrate the richness of this dataset by training state-of-the-art models for three chal…
University of Milan-Bicocca（米兰比可卡大学）；University of Pavia（帕维亚大学）
In this work we describe a Convolutional Neural Network (CNN) to accurately predict the scene illumination. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods. The network consists of one convolutional layer with max pooling, one fully connected layer and three output nodes. Within the network structure, feature learning and regression are integrated into one optimization process, which leads to a more effective model for estimating scene illumination. This approach achieves state-of-the-art performa…
Institute for Information Transmission Problems, RAS（俄罗斯科学院信息传输问题研究所）；University of Zagreb（萨格勒布大学）
Computational color constancy has the important task of reducing the influence of the scene illumination on the object colors. As such, it is an essential part of the image processing pipelines of most digital cameras. One of the important parts of the computational color constancy is illumination estimation, i.e. estimating the illumination color. When an illumination estimation method is proposed, its accuracy is usually reported by providing the values of error metrics obtained on the images of publicly available datasets. However, over time it has been shown that many of these datasets hav…
University of Amsterdam（阿姆斯特丹大学）；DUniversum（3D宇宙公司）
We investigate the use of photometric invariance and deep learning to compute intrinsic images (albedo and shading). We propose albedo and shading gradient descriptors which are derived from physics-based models. Using the descriptors, albedo transitions are masked out and an initial sparse shading map is calculated directly from the corresponding RGB image gradients in a learning-free unsupervised manner. Then, an optimization method is proposed to reconstruct the full dense shading map. Finally, we integrate the generated shading map into a novel deep learning framework to refine it and also…
Institute for Information Transmission Problems, RAS（俄罗斯科学院信息传输问题研究所）；National Research University The Higher School of Economics（国立研究型大学高等经济学院）；Gideon Brothers（吉迪恩兄弟公司）；Faculty of Electrical Engineering and Computing, University of Zagreb（萨格勒布大学电气工程与计算学院）；Nanjing University（南京大学）；Image Processing Systems Institute of RAS（俄罗斯科学院图像处理系统研究所）；Huawei MultiMedia Team（华为多媒体团队）；University of Milano – Bicocca（米兰比可卡大学）
Illumination estimation is the essential step of computational color constancy, one of the core parts of various image processing pipelines of modern digital cameras. Having an accurate and reliable illumination estimation is important for reducing the illumination influence on the image colors. To motivate the generation of new ideas and the development of new algorithms in this field, the 2nd Illumination estimation challenge~(IEC\#2) was conducted. The main advantage of testing a method on a challenge over testing in on some of the known datasets is the fact that the ground-truth illuminati…
We review the AIM 2020 challenge on virtual image relighting and illumination estimation. This paper presents the novel VIDIT dataset used in the challenge and the different proposed solutions and final evaluation results over the 3 challenge tracks. The first track considered one-to-one relighting; the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation (i.e., light source position). The goal of the second track was to estimate illumination settings, namely the color temperature and orientation, from a given image. Lastly, the third…
In this work, we demonstrate that receptive fields in 3D pose estimation can be effectively specified using optical flow. We introduce adaptive receptive fields, a simple and effective method to aid receptive field selection in pose estimation models based on optical flow inference. We contrast the performance of a benchmark state-of-the-art model running on fixed receptive fields with their adaptive field counterparts. By using a reduced receptive field, our model can process slow-motion sequences (10x longer) 23% faster than the benchmark model running at regular speed. The reduction in comp…
Boston University（波士顿大学）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）
While pose estimation is an important computer vision task, it requires expensive annotation and suffers from domain shift. In this paper, we investigate the problem of domain adaptive 2D pose estimation that transfers knowledge learned on a synthetic source domain to a target domain without supervision. While several domain adaptive pose estimation models have been proposed recently, they are not generic but only focus on either human pose or animal pose estimation, and thus their effectiveness is somewhat limited to specific scenarios. In this work, we propose a unified framework that genera…
