In this paper, we formulate the color constancy task as an image-to-image translation problem using GANs. By conducting a large set of experiments on different datasets, an experimental survey is provided on the use of different types of GANs to solve for color constancy i.e. CC-GANs (Color Constancy GANs). Based on the experimental review, recommendations are given for the design of CC-GAN architectures based on different criteria, circumstances and datasets.
More recent work uses powerful deep CNN models to approach the color constancy problem. @cite_26 uses an end-to-end deep model to estimate the illuminant. @cite_14 extracts patch features from a pre-trained CNN and feed them to a regression module to estimate the scene illumination. Both works provide state-of-the-art results supported by powerful CNN models. In another work, @cite_7 formulates the problem as an illumination classification problem using deep learning. @cite_24 proposes a two-branch deep CNN. One generates multiple hypotheses of the illuminant of a given patch. The other branch adaptively selects the best hypothesis. Moreover, @cite_35 estimates the local illuminant color of image patches. @cite_42 proposes a method to weight the input patches to differentiate between semantically valuable and semantically ambiguous local regions. Finally, @cite_30 exploits multiple frames over time within a deep learning framework. On the other hand, @cite_33 bypasses the illuminant color estimation step and directly learns a mapping from the input image to the white balanced image in an end-to-end fashion.
@cite_30 @cite_35 @cite_14 @cite_26 @cite_33 @cite_7 @cite_42 @cite_24
2777263104 1950407018 2951446647 2306965214 2896707699 2519060067
We introduce a novel formulation of temporal color constancy which considers multiple frames preceding the frame for which illumination is estimated. We propose an end-to-end trainable recurrent color constancy network – the RCC-Net – which exploits convolutional LSTMs and a simulated sequence to learn compositional representations in space and time. We use a standard single frame color constancy benchmark, the SFU Gray Ball Dataset, which can be adapted to a temporal setting. Extensive experiments show that the proposed method consistently outperforms single-frame state-of-the-art methods and their temporal variants. In this paper, we present a three-stage method for the estimation of the color of the illuminant in RAW images. The first stage uses a convolutional neural network that has been specially designed to produce multiple local estimates of the illuminant. The second stage, given the local estimates, determines the number of illuminants in the scene. Finally, local illuminant estimates are refined by non-linear local aggregation, resulting in a global estimate in case of single illuminant. An extensive comparison with both local and global illuminant estimation methods in the state of the art, on standard data sets with single and multiple illuminants, proves the effectiveness of our method. 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 performance on a standard dataset of RAW images. Preliminary experiments on images with spatially varying illumination demonstrate the stability of the local illuminant estimation ability of our CNN. Computational color constancy aims to estimate the color of the light source. The performance of many vision tasks, such as object detection and scene understanding, may benefit from color constancy by estimating the correct object colors. Since traditional color constancy methods are based on specific assumptions, none of those methods can be used as a universal predictor. Further, shallow learning schemes are used for training-based color constancy approaches, suffering from limited learning capacity. In this paper, we propose a framework using Deep Neural Networks (DNNs) to obtain an accurate light source estimator to achieve color constancy. We formulate color constancy as a DNN-based regression approach to estimate the color of the light source. The model is trained using datasets of more than a million images. Experiments show that the proposed algorithm outperforms the state-of-the-art by 9 . Especially in cross dataset validation, reducing the median angular error by 35 . Further, in our implementation, the algorithm operates at more than @math fps during Most popular color constancy algorithms assume that the light source obeys a uniform distribution across the scene. However, in the real world, the illuminations can vary a lot according to their spatial distribution. To overcome this problem, in this paper, we adopt a method based on a full end-to-end deep neural model to directly learn a mapping from the original image to the corresponding well-colored image. With this formulation, the network is able to determine pixel-wise illumination and produce a final visually compelling image. The training and evaluation of the network were performed on a standard dataset of two-dominant-illuminants. In this dataset, this approach achieves state-of-the-art performance. Besides, the main architecture of the network simply consists of a stack of fully convolutional blocks which can take the input of arbitrary size and produce correspondingly-sized output with effective learning. The experimental result shows that our customized loss function can help to reach a better performance than simply using MSE. Illuminant estimation to achieve color constancy is an ill-posed problem. Searching the large hypothesis space for an accurate illuminant estimation is hard due to the ambiguities of unknown reflections and local patch appearances. In this work, we propose a novel Deep Specialized Network (DS-Net) that is adaptive to diverse local regions for estimating robust local illuminants. This is achieved through a new convolutional network architecture with two interacting sub-networks, i.e. an hypotheses network (HypNet) and a selection network (SelNet). In particular, HypNet generates multiple illuminant hypotheses that inherently capture different modes of illuminants with its unique two-branch structure. SelNet then adaptively picks for confident estimations from these plausible hypotheses. Extensive experiments on the two largest color constancy benchmark datasets show that the proposed ‘hypothesis selection’ approach is effective to overcome erroneous estimation. Through the synergy of HypNet and SelNet, our approach outperforms state-of-the-art methods such as [1, 2, 3].