Computationally efficient adaptive color correction

Colour correction is the process of converting camera dependent RGB values to a camera independent standard colour space such as CIE XYZ. Regression methods — linear, polynomial, and root-polynomial least-squares — are traditionally used to solve for the colour correction transform. More recently neural net solutions for colour correction have been developed. This paper begins with the observation that the neural net solution — while delivering better colour correction accuracy compared to the simple (and widely deployed) 3×3 linear correction matrix approach — is not exposure invariant. That is to say, the network is tuned to mapping RGBs to XYZs for a fixed exposure level and when this exposure level changes, its performance degrades (and it delivers less accurate colour correction compared to the 3x3 matrix approach which is exposure invariant). We develop two remedies to the exposure variation problem. First, we augment the data we use to train the network to include responses for many different exposures. Concomitantly, the trained network is robust to a changing exposure. Second, we redesign the network so, by construction, it is exposure invariant. Experiments demonstrate that, by adopting either approach, Neural Network colour correction can be made exposure invariant.

This paper aims at presenting the complexity of the process of image target-based color correction (CC). We present issues encountered from acquisition to rendering using colorimetric traditional tools. Target-based CC can be seen as an optimization problem. We have tested SAT and HUE adaptive fine tuning (SHAFT) an automated framework for target-based CC. A key element of SHAFT is an iterative CIEDE2000 variation comparison between a reference and target image. In this work we replace the standard CIEDE2000 with the Euclidean color-difference formula for small-medium color differences in log-compressed Optical Society of America's Committee on Uniform Color Scales (OSA-UCS) space. Results are presented using both formulae. A discussion on the complexity of scene color departures and correction performances concludes the paper. The effect of real scene complexity is shown and how colors are subject to disordered shifts in the color space. Because of this complexity, the role of the CC method as a different color error minimizer emerges.

Color space normalization: Enhancing the discriminating power of color spaces for face recognition

Color correction is a necessary method step in the objectification of tongue diagnosis. Conventional polynomial regression is widely used in the online color correction, but it is easily influenced by the lighting source. In order to meet the requirements of different lighting conditions and maintain the accuracy of color correction, the Root-Polynomial Color Correction algorithm is used in this paper. This study established mapping between collected RGB value and standard RGB value through the calibration of the X-rite Color Checker. Finally, the CIELab color difference(ΔE) is calculated to verify the color correction effect. The color of the corrected images is close to the true color of the tongue. The average ΔE is less than 5, and the average algorithm calculation time t is less than 0.1 s. The experimental results show that RPCC can adapt to different lighting environments, and the color correction chromatic error is small.

Color correction is an essential image processing operation that transforms a camera-dependent RGB color space to a standard color space, e.g., the XYZ or the sRGB color space. The color correction is typically performed by multiplying the camera RGB values by a color correction matrix, which often amplifies image noise. In this paper, we propose an effective color correction pipeline for a noisy image. The proposed pipeline consists of two parts; the color correction and denoising. In the color correction part, we utilize spatially varying color correction (SVCC) that adaptively calculates the color correction matrices for each local image block considering the noise effect. Although the SVCC can effectively suppress the noise amplification, the noise is still included in the color corrected image, where the noise levels spatially vary for each local block. In the denoising part, we propose an effective denoising framework for the color corrected image with spatially varying noise levels. Experimental results demonstrate that the proposed color correction pipeline outperforms existing algorithms for various noise levels.

We present an efficient image stitching approach for creating high-quality continuous circular 360- degree panoramic images. We address the problem of color and luminance correction for long, 360-degree image sequences, where the source images have very different colors and luminance. Color matching is used for minimizing both the color differences of neighboring images and the overall color correction over the whole sequence. We perform gamma correction for the luminance component and linear correction for the chrominance components of the source images. The color consistency problem of 360-degree panoramic images is addressed by color correspondence and color difference distribution processes. We implement the stitching approach in a panoramic imaging system for creating high-resolution and high-quality panoramic images on mobile phones.

The color images produced by digital cameras are usually not in conformity with their intrinsic colors. This will seriously affect computer-aided medical facial image analysis because it is on the basis of accurate rendering of color information. To solve that, we propose an optimized color correction scheme for medical facial images based on a comprehensive study in various aspects: color spaces, color patches and color correction algorithms. Firstly, we utilize undistorted facial images to demarcate complexion gamut. Secondly, we choose the whole color patches located inside the range of complexion gamut as our validation samples and select the most crucial color patches from the gamut as our optimized training samples, followed by the optimization criteria. Thirdly, we select an adaptive target device-independent color space for medical facial images color correction task. Finally, we evaluate the performance of three most popular color correction algorithms, and select the most suitable one to build our final regression model. Qualitative and quantitative experimental results show that the proposed scheme is superior to that based on the ColorChecker 24 and performs very closely to the benchmark training error results while only 24 colors are involved. More importantly, the acquisition environment is well-designed with the system color chromatic aberration of 5.3369, which is quite small and hard to improve, but the color difference still can be perceived by human vision. Through our optimized color correction scheme, we succeed in reducing the color chromatic aberration to 1.5898. Furthermore, the corrected facial images are more consistent with the observed colors by human beings. Compared with the previous works, our color correction scheme is characterized by mission dependence and statistical reliability. Besides, the optimized color correction model has low complexity and high accuracy. All of these features make this scheme distinctive from previous color correction methods and effective for medical facial images color correction task.

Colour correction is the problem of mapping the sensor responses measured by a camera to the display-encoded RGBs or to a standard colour space such as CIE XYZ. In regression-based colour correction, camera RAW RGBs are mapped according to a simple formula (e.g. a linear mapping). Regression methods include least squares, polynomial and root-polynomial approaches. More recently, researchers have begun to investigate how neural networks can be used to solve the colour correction problem. _x005F_x000D_ _x005F_x000D_ In this paper, we investigate the relative performance of regression versus a neural network approach. While we find that the latter approach performs better than simple least-squares the performance is not as good as that delivered by either root-polynomial or polynomial regression. The root-polynomial approach has the advantage that it is also exposure invariant. In contrast, the Neural Network approach delivers poor colour correction when the exposure changes.

Color correction is one of essential stages in image processing, which plays an important role during image acquisition or pre-processing to produce a better color quality, before being used in further process. This paper proposes a new method for color correction using an improved linear regression algorithm based on a stepwise model. This proposed method is designed for assessing a series of discrete color levels, for instance in a leaf color chart. Color chart as a reference image is used for controlling color levels of a captured image or calibrating the image sensor. The experiment is conducted in L∗a∗b∗ color space, therefore a transformation from RGB into L∗a∗b∗ is needed at the first phase. The best matched color level between reference and captured image will be selected by k-Means clustering method. Chosen color levels are used for constructing linear regression function. This function is applied as well for removing outlier among color levels. To ensure the result of this color correction does not depend on lighting condition, the color constancy algorithm is acquired. Gray World and White Patch are chosen for color constancy methods. Compared to ordinary linear regression and color correction without adding color constancy, the combination of Gray World and improved linear regression algorithm based on stepwise model shows the best result in almost entire datasets in various lighting conditions.

Color correction is one of the most essential camera imaging operations that transforms a camera-specific RGB color space to a standard color space, typically the XYZ or the sRGB color space. Linear color correction (LCC) and polynomial color correction (PCC) are two widely used methods; they perform the color space transformation using a color correction matrix. Owing to the use of high-order terms, PCC generally achieves lower colorimetric errors than LCC. However, PCC amplifies noise more severely than LCC. Consequently, for noisy images, there exists a trade-off between LCC and PCC regarding color fidelity and noise amplification. We propose a color correction framework called tunable color correction (TCC) that enables us to tune the color correction matrix between the LCC and the PCC models. We also derive a mean squared error calculation model of PCC that enables us to select the best trade-off balance in the TCC framework. We experimentally demonstrate that TCC effectively balances the trade-off for noisy images and outperforms LCC and PCC. We also generalize TCC to multispectral cases and demonstrate its effectiveness by taking the color correction for an RGB-near-infrared sensor as an example.

The camera function of a smartphone can be used to quantitatively detect urine parameters anytime, anywhere. However, the color captured by different cameras in different environments is different. A method for color correction is proposed for a urine test strip image collected using a smartphone. In this method, the color correction model is based on the color information of the urine test strip, as well as the ambient light and camera parameters. Conv-TabNet, which can focus on each feature parameter, was designed to correct the color of the color blocks of the urine test strip. The color correction experiment was carried out in eight light sources on four mobile phones. The experimental results show that the mean absolute error of the new method is as low as 2.8±1.8, and the CIEDE2000 color difference is 1.5±1.5. The corrected color is almost consistent with the standard color by visual evaluation. This method can provide a technology for the quantitative detection of urine test strips anytime and anywhere.

Compared with images taken in the air, underwater images are susceptible due to the adverse effects of complex water conditions, such as absorption, attenuation, scattering, various noises, and uneven lighting, etc. These effects severely reduce the quality of underwater imaging and give rise to many problems among which is image color distortion. To fully acknowledge the underwater scenes, it is important to acquire the images with accurate colors, especially for marine rescue, underwater target recognition, and marine ecological research. To address this problem, this paper proposes an underwater imaging technology based on a light field camera array. The application of this technology can not only perform accurate color correction processing on the captured images but also expand the dynamic range of underwater images, thereby obtain enhanced high-quality, true-color underwater images. The experimental system in this paper contains a 3*3 camera array that simultaneously captures images of underwater targets with different exposure times. As the light attenuation coefficient of different wavelengths in water varies with the wavelength, during the color restoration process, we measure the relative value of the underwater spectral power distribution concerning the air, calculate the tristimulus value change according to the colorimetric principle, and convert the tristimulus value change in the CIEXYZ color space to the CIE La*b* color space. The color on the underwater images obtained by the camera array is then compensated. Then, in the image fusion process, images with different exposure are used to recover the response function of the imaging process, and multiple images are fused into a single, high-dynamic-range radiance map using high quality tone mapping technique in the radiance value dynamic display high-contrast images on devices with limited range. Experimental results show that our light-field imaging color correction and dynamic range expansion processing methods have obvious advantages over single cameras in underwater color correction and underwater moving target imaging detection, which provide advanced technology and equipment for underwater in-situ applications.

This paper proposes an automatic image correction method for portrait photographs, which promotes consistency of facial skin color by suppressing skin color changes due to background colors. In portrait photographs, skin color is often distorted due to the lighting environment (e.g., light reflected from a colored background wall and over-exposure by a camera strobe). This color distortion is emphasized when artificially synthesized with another background color, and the appearance becomes unnatural. In our framework, we, first, roughly extract the face region and rectify the skin color distribution in a color space. Then, we perform color and brightness correction around the face in the original image to achieve a proper color balance of the facial image, which is not affected by luminance and background colors. Our color correction process attains natural results by using a guide image, unlike conventional algorithms. In particular, our guided image filtering for the color correction does not require a perfectly-aligned guide image required in the original guide image filtering method proposed by He et al. Experimental results show that our method generates more natural results than conventional methods on not only headshot photographs but also natural scene photographs. We also show automatic yearbook style photo generation as another application.

Colour correction is the process of converting RAW RGB pixel values of digital cameras to a standard colour space such as CIE XYZ. A range of regression methods including linear, polynomial and root-polynomial least-squares have been deployed. However, in recent years, various neural network (NN) models have also started to appear in the literature as an alternative to classical methods. In the first part of this paper, a leading neural network approach is compared and contrasted with regression methods. We find that, although the neural network model supports improved colour correction compared with simple least-squares regression, it performs less well than the more advanced root-polynomial regression. Moreover, the relative improvement afforded by NNs, compared to linear least-squares, is diminished when the regression methods are adapted to minimise a perceptual colour error. Problematically, unlike linear and root-polynomial regressions, the NN approach is tied to a fixed exposure (and when exposure changes, the afforded colour correction can be quite poor). We explore two solutions that make NNs more exposure-invariant. First, we use data augmentation to train the NN for a range of typical exposures and second, we propose a new NN architecture which, by construction, is exposure-invariant. Finally, we look into how the performance of these algorithms is influenced when models are trained and tested on different datasets. As expected, the performance of all methods drops when tested with completely different datasets. However, we noticed that the regression methods still outperform the NNs in terms of colour correction, even though the relative performance of the regression methods does change based on the train and test datasets.

As light travels under the deep water, it scatters and is absorbed, resulting in a loss of intensity and altered color perception—a phenomenon known as underwater light attenuation. Images captured under these low light conditions suffered from color distortions, as you go deeper, colors fade in this order: red, orange, and yellow, while green and blue become more prominent. The red channel experiences significant attenuation due to the scattering properties of light under the deep water. As a consequence, deep water images often display noticeable color casts. Researchers encounter various challenges when enhancing low-light underwater images, such as reduced contrast, detail loss, artifacts, noise, and color distortion. In this paper, we present a novel Adaptive Color and Light Correction (ACLC) method for color correction and an Intuitionistic Fuzzy Generator (IFG) for enhancing low-light underwater images. The proposed Adaptive Color and Light Correction (ACLC) method tackles color casts on individual pixels by considering the scene depth. The proposed Intuitionistic Fuzzy Generator (IFG) method balances the image contrast by computing an intuitionistic fuzzy image representation using the proposed IFG approach. Experimental results reveal that the proposed approach significantly improves the color quality and contrast of the output image. The proposed ACLC and IFG methods exceed existing underwater color correction and low-light image enhancement techniques in visual and quantitative evaluations, as evidenced by extensive experimentation on well-established underwater image datasets, such as UIEB, Ocean dark, and LSUI.