Guiding the Illumination Estimation Using the Attention Mechanism

Koščević, Karlo; Subašić, Marko; Lončarić, Sven

doi:10.1145/3379310.3379329

Cited by 8 publications

(6 citation statements)

References 37 publications

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“…The average execution time on the test set using only one core was 2.04 seconds per input image. The proposed model has 14,716,227 weights which is less compared to deep learning-based illumination estimations methods evaluated on the Cube+ dataset in [19], [20], [22], which all use VGG16 network structure for feature extraction, but have more complex additional layer structures, such as attention blocks, or have multiple instances of the same network structure with different weights.…”

Section: Methods Performance 1) Comparison With Existing Illuminatimentioning

confidence: 99%

“…The earliest deep learning architectures for illumination estimation were very shallow, containing only a few convolutional and fully connected layers [16], [17]. Content-based convolutional neural networks that combine weighted local illumination estimations have been proposed in [18]- [20]. In [21], [22], illumination estimation was cast into a deep learning classification problem.…”

Section: A Illumination Estimationmentioning

confidence: 99%

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Iterative Convolutional Neural Network-Based Illumination Estimation

2021

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In the image processing pipelines of digital cameras, one of the first steps is to achieve invariance in terms of scene illumination, namely computational color constancy. Usually, this is done in two successive steps which are illumination estimation and chromatic adaptation. The illumination estimation aims at estimating a three-dimensional vector from image pixels. This vector represents the scene illumination, and it is used in the chromatic adaptation step, which aims at eliminating the bias in image colors caused by the color of the illumination. An accurate illumination estimation is crucial for successful computational color constancy. However, this is an ill-posed problem, and many methods try to comprehend it with different assumptions. In this paper, an iterative method for estimating the scene illumination color is proposed. The method calculates the illumination vector by a series of intermediate illumination estimations and chromatic adaptations of an input image using a convolutional neural network. The network has been trained to iteratively compute intermediate incremental illumination estimates from the original image. Incremental illumination estimates are combined by per element multiplication to obtain the final illumination estimation. The approach is aimed to reduce large estimation errors usually occurring with highly saturated light sources. Experimental results show that the proposed method outperforms the vast majority of illumination estimation methods in terms of median angular error. Moreover, in terms of worst-performing samples, i.e., the samples for which a method errs the most, the proposed method outperforms all other methods by a margin of more than 18% with respect to the mean of estimation errors in the third quartile.

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Section: Methods Performance 1) Comparison With Existing Illuminatimentioning

confidence: 99%

Section: A Illumination Estimationmentioning

confidence: 99%

Iterative Convolutional Neural Network-Based Illumination Estimation

2021

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“…In [46], a convolutional neural network was used to cast the illumination estimation problem into an illumination classification problem, which computes the global illumination based on the results of k-means clustering and classification probabilities. In [47]- [49], convolutional neural networks with weighted local illumination pooling have been proposed. A major drawback of the aforementioned deep-learning methods is that they are sensor-dependent.…”

Section: Related Workmentioning

confidence: 99%

“…For illumination estimation, the convolutional neural network proposed in [49] was used. It is a fully convolutional neural network.…”

Section: B Illumination Estimationmentioning

confidence: 99%

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Deep Learning-Based Illumination Estimation Using Light Source Classification

2020

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Color constancy is one of the key steps in the process of image formation in digital cameras. Its goal is to process the image so that there is no influence of illumination color on the colors of objects and surfaces. To capture the target scene colors as accurately as possible, it is crucial to estimate the illumination vector with high accuracy. Unfortunately, the illumination estimation is an ill-posed problem, and solving it most often relies on assumptions. To date, various assumptions have been proposed, which resulted in a wide variety of illumination estimation methods. Statistics-based methods have shown to be appropriate for hardware implementation, but learning-based methods achieve state-of-the-art results, especially those that use deep neural networks. The large learning capacities and generalization abilities of deep neural networks can be used to develop the illumination estimation methods, which are more general and precise. This approach avoids introducing many new assumptions, which often only work in some specific situations. In this paper, a new method for illumination estimation based on light source classification is proposed. In the first step, the set of possible illuminations is reduced by classifying the input image in one of three classes. The classes include images captured in outdoor scenes under natural illuminations, images captured in outdoor scenes under artificial illuminations, and images captured in indoor scenes under artificial illuminations. In the second step, a deep illumination estimation network, which is trained exclusively on images in the class that was predicted in the first step, is applied to the input image. Dividing the illumination space into smaller regions makes the training of illumination estimation networks simpler because the distribution of image scenes and illuminations is less diverse. The experiments on the Cube+ image dataset have shown the median illumination estimation error of 1.27 • , which is an improvement of more than 25% compared to the use of the single network for all illuminations.

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Using convolutional neural network models illumination estimation according to light colors

Büyükarikan

Ülker

2022

Optik

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Guiding the Illumination Estimation Using the Attention Mechanism

Cited by 8 publications

References 37 publications

Iterative Convolutional Neural Network-Based Illumination Estimation

Iterative Convolutional Neural Network-Based Illumination Estimation

Deep Learning-Based Illumination Estimation Using Light Source Classification

Using convolutional neural network models illumination estimation according to light colors

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