Abstract White balance (WB) correction is critical for accurate color reproduction in digital images, especially under complex, multi-illuminant lighting conditions. Traditional methods, such as the Gray-World assumption, rely on global statistics and struggle in real-world, non-uniform lighting scenarios. Modern deep learning approaches, including convolutional and attention-based architectures, have significantly advanced WB correction but often fail to explicitly account for higher-order feature distribution statistics, which may limit their robustness in challenging environments. This study introduces a novel framework that leverages Exact Feature Distribution Matching (EFDM) as a loss objective to align feature distributions across multiple moments, including mean, variance, skewness, and kurtosis. By modeling lighting as a style factor, the method explicitly addresses distributional shifts caused by complex illumination, offering a robust solution for WB correction. The framework integrates EFDM with a Vision Transformer architecture, enabling precise handling of global and local lighting variations. Extensive experiments on the large-scale multi-illuminant (LSMI) dataset demonstrate the superiority of the proposed approach over state-of-the-art methods and commonly used loss functions when applied to the same architecture. Qualitative and quantitative evaluations highlight its effectiveness in achieving perceptually accurate WB correction, particularly in multi-illuminant environments. By bridging statistical modeling with modern deep learning, this work establishes the critical role of feature distribution alignment in advancing WB correction and sets a new benchmark for robustness and generalization in complex lighting scenarios.

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 different WB settings. Proposed network utilizes the style information, extracted from the scene by a multi-head style extraction module. AWB correction is completed after blending these weighting maps and the scene. Experiments on single- and mixed-illuminant datasets demonstrate that our proposed method achieves promising correction results when compared to the recent works. This shows that the lighting in the scenes with multiple illuminations can be modeled by the concept of style. Source code and trained models are available on https://github.com/birdortyedi/lighting-as-style-awb-correction.

Capsules are grouping of neurons that allow to represent sophisticated information of a visual entity such as pose and features. In the view of this property, Capsule Networks outperform CNNs in challenging tasks like object recognition in unseen viewpoints, and this is achieved by learning the transformations between the object and its parts with the help of high dimensional representation of pose information. In this paper, we present Quaternion Capsules (QCN) where pose information of capsules and their transformations are represented by quaternions. Quaternions are immune to the gimbal lock, have straightforward regularization of the rotation representation for capsules, and require less number of parameters than matrices. The experimental results show that QCNs generalize better to novel viewpoints with fewer parameters, and also achieve onpar or better performances with the state-of-the-art Capsule architectures on well-known benchmarking datasets. Our code is available1.

Test oracles differentiate between the correct and incorrect system behavior. Automation of test oracles for visual output systems mainly involves image comparison, where a snapshot of the output is compared with respect to a reference image. Hereby, the captured snapshot can be subject to variations such as scaling and shifting. These variations lead to incorrect evaluations. Existing approaches employ computer vision techniques to address a specific set of variations. In this article, we introduce ADVISOR, an adjustable framework for test oracle automation of visual output systems. It allows the use of a flexible combination and configuration of computer vision techniques. We evaluated a set of valid configurations with a benchmark dataset collected during the tests of commercial digital TV systems. Some of these configurations achieved up to 3% better overall accuracy with respect to state-of-the-art tools. Further, we observed that there is no configuration that reaches the best accuracy for all types of image variations. We also empirically investigated the impact of significant parameters. One of them is a threshold regarding image matching score that determines the final verdict. This parameter is automatically tuned by offline training. We evaluated runtime performance as well. Results showed that differences among the ADVISOR configurations and state-of-the-art tools are in the order of seconds per image comparison.

Runtime specialization is used for optimizing programs based on partial information available only at runtime. In this paper we apply autotuning on runtime specialization of Sparse Matrix-Vector Multiplication to predict a best specialization method among several. In 91% to 96% of the predictions, either the best or the second-best method is chosen. Predictions achieve average speedups that are very close to the speedups achievable when only the best methods are used. By using an efficient code generator and a carefully designed set of matrix features, we show the runtime costs can be amortized to bring performance benefits for many real-world cases.