Nikola Banic

Light Random Sprays Retinex: Exploiting the Noisy Illumination Estimation

In this letter, Light Random Sprays Retinex (LRSR), an improvement of the Random Sprays Retinex (RSR) algorithm is proposed. RSR is a white balancing algorithm for achieving local color constancy and image enhancement by using random sprays of the same size. The main problem of the original RSR is that the lower the number and size of the sprays, the greater the noise in the resulting image, which means that the number and size of sprays have to be relatively high in order to reduce the noise leading to a higher computation cost. The proposed improved algorithm is based on a new method to remove the noise in the resulting image thereby allowing only one spray of a smaller size to be used resulting in lower computation cost. By using interpolation the computation cost is reduced even further without a noticeable perceptual difference. The improvement is tested on a public database and is shown to outperform the original RSR in image quality and computation cost. The source code is available at http://www.fer.unizg.hr/ipg/resources/color_constancy/.

Smart light random memory sprays Retinex: a fast Retinex implementation for high-quality brightness adjustment and color correction

Removing the influence of illumination on image colors and adjusting the brightness across the scene are important image enhancement problems. This is achieved by applying adequate color constancy and brightness adjustment methods. One of the earliest models to deal with both of these problems was the Retinex theory. Some of the Retinex implementations tend to give high-quality results by performing local operations, but they are computationally relatively slow. One of the recent Retinex implementations is light random sprays Retinex (LRSR). In this paper, a new method is proposed for brightness adjustment and color correction that overcomes the main disadvantages of LRSR. There are three main contributions of this paper. First, a concept of memory sprays is proposed to reduce the number of LRSRs per-pixel operations to a constant regardless of the parameter values, thereby enabling a fast Retinex-based local image enhancement. Second, an effective remapping of image intensities is proposed that results in significantly higher quality. Third, the problem of LRSRs halo effect is significantly reduced by using an alternative illumination processing method. The proposed method enables a fast Retinex-based image enhancement by processing Retinex paths in a constant number of steps regardless of the path size. Due to the halo effect removal and remapping of the resulting intensities, the method outperforms many of the well-known image enhancement methods in terms of resulting image quality. The results are presented and discussed. It is shown that the proposed method outperforms most of the tested methods in terms of image brightness adjustment, color correction, and computational speed.

Improving the white patch method by subsampling

In this paper an improvement of the white patch method, a color constancy algorithm, is proposed. The improved method is tested on several benchmark databases and it is shown to outperform the baseline white patch method in terms of accuracy. On the benchmark database it also outperforms most of the other methods and its great execution speed makes it suitable for hardware implementation. The results are presented and discussed and the source code is available at http://www.fer.unizg.hr/ipg/resources/color constancy/.

Color Dog - Guiding the Global Illumination Estimation to Better Accuracy

An important part of image enhancement is color constancy, which aims to make image colors invariant to illumination. In this paper the Color Dog (CD), a new learning-based global color constancy method is proposed. Instead of providing one, it corrects the other methods’ illumination estimations by reducing their scattering in the chromaticity space by using a its previously learning partition. The proposed method outperforms all other methods on most high-quality benchmark datasets. The results are presented and discussed.

Color Rabbit: Guiding the distance of local maximums in illumination estimation

In this paper the Color Rabbit (CR), a new low-level statistics-based color constancy algorithm for illumination estimation is proposed and tested. Based on the Color Sparrow (CS) algorithm it combines multiple local illumination estimations found by using a new approach into a global one. The algorithm is tested on several publicly available color constancy databases and it outperforms almost all other color constancy algorithms in terms of accuracy and execution speed.

Color Cat: Remembering Colors for Illumination Estimation

Having images look the same regardless of the scene illumination is a desirable feature called color constancy. In this paper the Color Cat (CC), a novel fast and accurate learning-based method for achieving computational color constancy is proposed. It learns and then uses the relationship between transformed color histograms and the regularity in the possible illumination colors. The proposed method is tested on a publicly available color constancy dataset and it is shown to outperform most of the other color constancy methods in terms of accuracy and computation cost. The results are presented and discussed. The source code is available at http://www.fer.unizg.hr/ipg/resources/color_constancy/.

Color Badger: A Novel Retinex-Based Local Tone Mapping Operator

In this paper a novel tone mapping operator (TMO) based on the Light Random Sprays Retinex (LRSR) algorithm is presented. TMOs convert high dynamic range (HDR) images to low dynamic range (LDR) images, which is often needed because of the display limitations of many devices. The proposed operator is a local operator, which retains the qualities of the LRSR and overcomes some of its weaknesses. The results of the execution speed and quality tests are presented and discussed and it is shown that on most of the test images the proposed operator is faster and in terms of quality as good as Durand’s TMO, one of the currently best TMOs. The C++ source code of the proposed operator is available at http://www.fer.unizg.hr/ipg/resources/color_constancy/ .

Puma: A high-quality retinex-based tone mapping operator

Tone mapping is the process of compressing high dynamic range (HDR) images to obtain low dynamic range (LDR) images in order to display them on standard display devices. The methods that perform tone mapping also known as tone mapping operators (TMOs) can be global and process all luminances in the same way, or they can be local and process the luminances with respect to their closer neighborhood. While the former tend to be faster, the latter are known to produce results of significantly higher quality. In this paper perceptually-based tone mapping is combined with one of the latest Retinex-based methods to create a high-quality TMO. The new TMO requires only a constant number of steps per pixel and experimental results show that it outperforms all but one state-of-the-art TMOs in terms of tone mapped LDR image quality. The source code is available at http://www.fer.unizg.hr/ipg/resources/color_constancy/.

Improving the Tone Mapping Operators by Using a Redefined Version of the Luminance Channel

Tone mapping operators (TMOs) convert high dynamic range (HDR) images to low dynamic range (LDR) images and are important because of the limitations of many standard display devices. Even though the quality of the resulting LDR image mostly depends on TMO parameter values, in this paper it is shown that it can be further improved by using alternative definitions of the luminance channel, which TMOs process. A new model of the luminance channel calculation that increases the resulting LDR image quality is also proposed. The main advantage of the new model is that the TMOs that produce results of lower quality can be made to produce results of significantly higher quality.

Using the red chromaticity for illumination estimation

Achieving color invariance to illumination is known as color constancy and it is implemented in most digital cameras. There are statistics-based and learning-based computational color constancy methods and the latter ones are known to be more accurate. For a given image these methods extract some features and since for some methods calculating and processing these features can be computationally demanding, this renders such methods slow and very often impractical for hardware implementation. In this paper simple, yet very powerful features for color constancy based on the red chromaticity are presented. A new color constancy method is proposed and it is demonstrated how an existing one can be simplified. In both cases state-of-the-art results are achieved. The results are presented and discussed and the source code is available at http://www.fer.unizg.hr/ipg/resources/color constancy/.