Artificial Color Constancy via GoogLeNet with Angular Loss Function

Color Constancy is the ability of the human visual system to perceive colors unchanged independently of the illumination. Giving a machine this feature will be beneficial in many fields where chromatic information is used. Particularly, it significantly improves scene understanding and object recognition. In this paper, we propose transfer learning-based algorithm, which has two main features: accuracy higher than many state-of-the-art algorithms and simplicity of implementation. Despite the fact that GoogLeNet was used in the experiments, given approach may be applied to any CNN. Additionally, we discuss design of a new loss function oriented specifically to this problem, and propose a few the most suitable options

Color Constancy Adjustment using Sub-blocks of the Image

Extreme presence of the source light in digital images decreases the performance of many image processing algorithms, such as video analytics, object tracking and image segmentation. This paper presents a color constancy adjustment technique, which lessens the impact of large unvarying color areas of the image on the performance of the existing statistical based color correction algorithms. The proposed algorithm splits the input image into several non-overlapping blocks. It uses the Average Absolute Difference (AAD) value of each block’s color component as a measure to determine if the block has adequate color information to contribute to the color adjustment of the whole image. It is shown through experiments that by excluding the unvarying color areas of the image, the performances of the existing statistical-based color constancy methods are significantly improved. The experimental results of four benchmark image datasets validate that the proposed framework using Gray World, Max-RGB and Shades of Gray statistics-based methods’ images have significantly higher subjective and competitive objective color constancy than those of the existing and the state-of-the-art methods’ images

Color Constancy Algorithm for Mixed-illuminant Scene Images

The intrinsic properties of the ambient illuminant significantly alter the true colors of objects within an image. Most existing color constancy algorithms assume a uniformly lit scene across the image. The performance of these algorithms deteriorates considerably in the presence of mixed illuminants. Hence, a potential solution to this problem is the consideration of a combination of image regional color constancy weighing factors (CCWFs) in determining the CCWF for each pixel. This paper presents a color constancy algorithm for mixed-illuminant scene images. The proposed algorithm splits the input image into multiple segments and uses the normalized average absolute difference (NAAD) of each segment as a measure for determining whether the segment’s pixels contain reliable color constancy information. The Max-RGB principle is then used to calculate the initial weighting factors for each selected segment. The CCWF for each image pixel is then calculated by combining the weighting factors of the selected segments, which are adjusted by the normalized Euclidian distances of the pixel from the centers of the selected segments. Experimental results on images from five benchmark datasets show that the proposed algorithm subjectively outperforms the state-of-the-art techniques, while its objective performance is comparable to those of the state-of-the-art techniques

Color Constancy for Uniform and Non-uniform Illuminant Using Image Texture

Color constancy is the capability to observe the true color of a scene from its image regardless of the scene’s illuminant. It is a significant part of the digital image processing pipeline and is utilized when the true color of an object is required. Most existing color constancy methods assume a uniform illuminant across the whole scene of the image, which is not always the case. Hence, their performances are influenced by the presence of multiple light sources. This paper presents a color constancy adjustment technique that uses the texture of the image pixels to select pixels with sufficient color variation to be used for image color correction. The proposed technique applies a histogram-based algorithm to determine the appropriate number of segments to efficiently split the image into its key color variation areas. The K-means++ algorithm is then used to divide the input image into the pre-determined number of segments. The proposed algorithm identifies pixels with sufficient color variation in each segment using the entropies of the pixels, which represent the segment’s texture. Then, the algorithm calculates the initial color constancy adjustment factors for each segment by applying an existing statistics-based color constancy algorithm on the selected pixels. Finally, the proposed method computes color adjustment factors per pixel within the image by fusing the initial color adjustment factors of all segments, which are regulated by the Euclidian distances of each pixel from the centers of gravity of the segments. Experimental results on benchmark single- and multiple-illuminant image datasets show that the images that are obtained using the proposed algorithm have significantly higher subjective and very competitive objective qualities compared to those that are obtained with the state-of-the-art techniques

Color Constancy Using 3D Scene Geometry Derived From a Single Image

The aim of color constancy is to remove the effect of the color of the light source. As color constancy is inherently an ill-posed problem, most of the existing color constancy algorithms are based on specific imaging assumptions (e.g., gray-world and white patch assumption). In this paper, 3D geometry models are used to determine which color constancy method to use for the different geometrical regions (depth/layer) found in images. The aim is to classify images into stages (rough 3D geometry models). According to stage models, images are divided into stage regions using hard and soft segmentation. After that, the best color constancy methods are selected for each geometry depth. To this end, we propose a method to combine color constancy algorithms by investigating the relation between depth, local image statistics, and color constancy. Image statistics are then exploited per depth to select the proper color constancy method. Our approach opens the possibility to estimate multiple illuminations by distinguishing nearby light source from distant illuminations. Experiments on state-of-the-art data sets show that the proposed algorithm outperforms state-of-the-art single color constancy algorithms with an improvement of almost 50% of median angular error. When using a perfect classifier (i.e, all of the test images are correctly classified into stages); the performance of the proposed method achieves an improvement of 52% of the median angular error compared with the best-performing single color constancy algorithm

Color Constancy Using 3D Scene Geometry Derived From a Single Image

The aim of color constancy is to remove the effect of the color of the light source. As color constancy is inherently an ill-posed problem, most of the existing color constancy algorithms are based on specific imaging assumptions (e.g., gray-world and white patch assumption). In this paper, 3D geometry models are used to determine which color constancy method to use for the different geometrical regions (depth/layer) found in images. The aim is to classify images into stages (rough 3D geometry models). According to stage models, images are divided into stage regions using hard and soft segmentation. After that, the best color constancy methods are selected for each geometry depth. To this end, we propose a method to combine color constancy algorithms by investigating the relation between depth, local image statistics, and color constancy. Image statistics are then exploited per depth to select the proper color constancy method. Our approach opens the possibility to estimate multiple illuminations by distinguishing nearby light source from distant illuminations. Experiments on state-of-the-art data sets show that the proposed algorithm outperforms state-of-the-art single color constancy algorithms with an improvement of almost 50% of median angular error. When using a perfect classifier (i.e, all of the test images are correctly classified into stages); the performance of the proposed method achieves an improvement of 52% of the median angular error compared with the best-performing single color constancy algorithm