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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.

In this paper, we present a novel approach to the problem of reading residential meters using deep learning algorithms. As a starting point we use Faster R-CNN method and, to acquire more precise readings, we modify its functionality. As there were no databases for this kind of task, one had to be collected and properly annotated. This paper also provides a brief introduction to methods for image augmentation and a technique to augment annotated image dataset. For each part of the presented method as well as the whole method as one unit experiments were conducted to show the overall successfulness.

This paper reviews the NTIRE 2022 challenge on night photography rendering. The challenge solicited solutions that processed RAW camera images captured in night scenes to produce a photo-finished output image encoded in the standard RGB (sRGB) space. Given the subjective nature of this task, the proposed solutions were evaluated based on the mean opinions of viewers asked to judge the visual appearance of the results. Michael Freeman, a world-renowned photographer, further ranked the solutions with the highest mean opinion scores. A total of 13 teams competed in the final phase of the challenge. The proposed methods provided by the participating teams represent state-of-the-art performance in nighttime photography. Results from the various teams can be found here: https://nightimaging.org/

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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, two challenges on illumination estimation were conducted. The main advantage of testing a method on a challenge over testing it on some of the known datasets is the fact that the ground‐truth illuminations for the challenge test images are unknown up until the results have been submitted, which prevents any potential hyperparameter tuning that may be biased. The First illumination estimation challenge (IEC#1) had only a single task, global illumination estimation. The second illumination estimation challenge (IEC#2) was enriched with two additional tracks that encompassed indoor and two‐illuminant illumination estimation. Other main features of it are a new large dataset of images (about 5000) taken with the same camera sensor model, a manual markup accompanying each image, diverse content with scenes taken in numerous countries under a huge variety of illuminations extracted by using the SpyderCube calibration object, and a contest‐like markup for the images from the Cube++ dataset. This article focuses on the description of the past two challenges, algorithms which won in each track, and the conclusions that were drawn based on the results obtained during the first and second challenge that can be useful for similar future developments.