Color Transfer Method Research Articles

A Color Consistency Processing Method for HY-1C Images of Antarctica

The HY-1C satellite, as part of China’s optical satellite constellation for global ocean monitoring, monitors the ocean and coastal environment by the three broad visible bands of the Coastal Zone Imager (CZI) instrument. However, as a result of the sensor instrument noise, the atmospheric environment during imaging, and the shooting angle, the satellite images often show uneven illumination and inconsistent color between neighboring images. In this paper, according to the characteristics of the HY-1C CZI instrument, we propose a color consistency processing framework for coastal zone images of Antarctica. First of all, the high-frequency and low-frequency information of the image is separated by a statistical filter with simple clustering. The uneven lighting is then replaced by artificial lighting, which is globally uniform. Finally, the color difference between images is corrected by a color transfer method. In order to evaluate the color consistency results quantitatively, a new quantitative evaluation method is proposed. The experimental results for the coastal zone images of Antarctica show that the new processing framework can effectively eliminate the unevenness in the lighting and color. The mosaic results show a good performance in consistent lighting and tones, and the lack of visible mosaic lines proves the effectiveness of the proposed method. The quantitative evaluation analysis confirms the superiority of the proposed method over the Wallis method.

IDT and Color Transfer-Based Color Calibration for Images Taken by Different Cameras

Images of the same object taken by multiple different cameras should have the same color reproduction. However, the images sometimes show different color reproduction due to the individual differences of cameras or internal camera parameters automatically determined when the images are taken. Conventional color transfer methods can be used for unifying the color reproduction of images by transforming the color distribution of an image to that of a reference image. However, conventional methods do not always lead to a good color reproduction and sometimes result in the loss of color impression of original images. In the present paper, we propose a color calibration method for images of the same object taken by different cameras. Two color transfer methods are combined to realize color calibration without the loss of color impression of an original image. Resultant images obtained by the color transfer methods are appropriately mixed into an output image. In experiments, the proposed method is applied to a variety of images and the effectiveness of the proposed method is confirmed by subjective and objective evaluations.

High Quality Light Field Extraction and Post-Processing for Raw Plenoptic Data.

Light field technology has reached a certain level of maturity in recent years, and its applications in both computer vision research and industry are offering new perspectives for cinematography and virtual reality. Several methods of capture exist, each with its own advantages and drawbacks. One of these methods involves the use of handheld plenoptic cameras. While these cameras offer freedom and ease of use, they also suffer from various visual artefacts and inconsistencies. We propose in this paper an advanced pipeline that enhances their output. After extracting sub-aperture images from the RAW images with our demultiplexing method, we perform three correction steps. We first remove hot pixel artefacts, then correct colour inconsistencies between views using a colour transfer method, and finally we apply a state of the art light field denoising technique to ensure a high image quality. An in-depth analysis is provided for every step of the pipeline, as well as their interaction within the system. We compare our approach to existing state of the art sub-aperture image extracting algorithms, using a number of metrics as well as a subjective experiment. Finally, we showcase the positive impact of our system on a number of relevant light field applications.

Color Transfer With Salient Features Mapping via Attention Maps Between Images

Color transfer is a useful image editing technology that transfers the color style of a reference image to a source image. In this paper, a novel automatic perception color transfer method based on salient features mapping was proposed. Firstly, the saliency maps of the reference image and source image are built to predict the human visual attention regions. Secondly, the gradient map is calculated by convolving the image pair with Sobel filter. Thirdly, the saliency map and gradient map are combined to produce a weighted attention map. Fourthly, by thresholding the attention map the reference image and the source image are segmented into regions of salient and non-salient pixels, and mean and standard deviation of these two regions are calculated in the three channels of the YUV space, respectively. Finally, on the salient and non-salient pixels an execution similar to Reinhard's is performed, and the two results are combined to generate the final output. The experiments result show the superiority of our method by its pleasant natural appearance with color similarity and image details preservation, high time efficiency and easy implementation.

Applicability of colour transfer techniques in Twelve fringe photoelasticity (TFP)

Colour adaptation techniques are useful in extending the method of Twelve fringe photoelasticity (TFP) to solve problems in industries as one can minimise the generation of specific calibration tables for each experiment. Image artefacts affect the functioning of existing colour adaptation techniques affecting the fringe order demodulation. Colour transfer is a popular technique in the field of image processing to tackle colour mismatch between images. In this paper, the applicability of two colour transfer strategies in the context of TFP is explored, of which one is selected for implementation. A comparative study on the performance of the novel colour transfer method with the existing colour adaptation techniques is carried out.

Stereoscopic color transfer and disparity remapping based on selected object

Color transfer has been a hot research issue in the field of image processing and computer vision in recent years. The main purpose is to transfer the color of a target image to source image so that the source image has the same or similar color features with the target image. In practical applications for the color transfer of binocular stereoscopic images, the user may only need to transfer the color of the selected object while keeping the background color unchanged. For this purpose, a color transfer method based on the selected object is proposed in this paper. In the method, by assigning the object of the image by user, the accurate object is segmented via graph cut, and the probability density curves of color distribution between the selected object and the target image are matched to accomplish the color transfer. In order to enhance the viewing experience provided for the user, a non-linear disparity optimization is performed after the color transfer operation. According to the histogram feature of disparity map, the disparity mapping function is calculated, and the target disparity is obtained to enhance the depth sensation of the selected object. The experimental results demonstrate that the combination of stereoscopic color transfer and disparity remapping effectively enhances the stereoscopic viewing experience.

Directive local color transfer based on dynamic look-up table

Color transfer in image processing usually suffers from misleading color mapping and loss of details. This paper presents a novel directive local color transfer method based on dynamic look-up table (D-DLT) to solve these problems in two steps. First, a directive mapping between the source and the reference image is established based on the salient detection and the color clusters to obtain directive color transfer intention. Then, dynamic look-up tables are created according to the color clusters to preserve the details, which can suppress pseudo contours and avoid detail loss. Subjective and objective assessments are presented to verify the feasibility and the availability of the proposed approach. Experimental results demonstrate that our proposed method has better performance on natural color images than classical color transfer algorithms. Furthermore, the reference image can be extended to color blocks instead of images.

Image-based translucency transfer through correlation analysis over multi-scale spatial color distribution

This paper introduces an image-based material transfer framework which only requires single input and reference images as an ordinary color transfer method. In contrast to previous material transfer methods, we focus on transferring the appearances of translucent objects. There are two challenging problems in such material transfer for translucent objects. First, the appearances of translucent materials are characterized by not only colors but also their spatial distribution. Unfortunately, the traditional color transfer methods can hardly handle the translucency because they only consider the colors of the objects. Second, temporal coherency in the transferred results cannot be handled by the traditional methods and furthermore by recent neural style transfer methods, as we demonstrated in this paper. To address these problems, we propose a novel image-based material transfer method based on the analysis of spatial color distribution. We focus on “subbands,” which represent multi-scale image structures, and find that the correlation between color distribution and subbands is a key feature for reproducing the appearances of translucent materials. Our method relies on a standard principal component analysis, which harmonizes the correlation of input and reference images to reproduce the translucent appearance. Considering the spatial color distribution in the input and reference images, our method can be naturally applied to video sequences in a frame-by-frame manner without any additional pre-/post-process. Through experimental analyses, we demonstrate that the proposed method can be applied to a broad variety of translucent materials, and their resulting appearances are perceptually similar to those of the reference images.

Saliency-aware inter-image color transfer for image manipulation

This paper proposes a novel saliency-aware inter-image color transfer method to perform image manipulation. Specifically, given the source image, the candidate images are first retrieved from a group of images with the same semantic category, and the corresponding saliency maps are obtained using an existing saliency model. Then, the inter-image color transfer method is proposed to transfer the colors of the high-saliency region in each candidate image to the target object region in the source image, for generating the manipulated image. Finally, from a set of manipulated images, the one with the highest weighted F-measure of its saliency map is selected as the final result. Experimental results show that the proposed method not only highlights objects effectively but also preserves the naturalness of images well, and consistently outperforms other image manipulation methods when viewing the manipulated images with or without the source image as the reference.

Probabilistic moving least squares with spatial constraints for nonlinear color transfer between images

The color of a scene may vary from image to image because the photographs are taken at different times, with different cameras, and under different camera settings. To align the color of a scene between images, we introduce a novel color transfer framework based on a scattered point interpolation scheme. Compared to the conventional color transformation methods that use a parametric mapping or color distribution matching, we solve for a full nonlinear and nonparametric color mapping in the 3D RGB color space by employing the moving least squares framework. We further strengthen the transfer with a probabilistic modeling of the color transfer in the 3D color space as well as spatial constraints to deal with mis-alignments, noise, and spatially varying illumination. Experiments show the effectiveness of our method over previous color transfer methods both quantitatively and qualitatively. In addition, our framework can be applied for various instances of color transfer such as transferring color between different camera models, camera settings, and illumination conditions, as well as for video color transfers.

Image colorization by fusion of color transfers based on DFT and variance features

Color transfer methods usually suffer from spatial color coherency problem. In order to address this problem, this paper develops a fused color transfer method for image colorization. Our idea is to design a local student’s t-test to screen the incoherent colors in the preliminary colorization results obtained by a simple color transfer method with DFT and variance features. Furthermore, we propose a variational fusion model to inpaint these incoherent colors and fuse the other useful colors together. We also present an efficient algorithm for solving the fusion model numerically, and show the convergence of the algorithm. Finally, experimental results are reported to demonstrate the effectiveness of the proposed method, and its performance is competitive with those of the other testing methods.

Photo2Silhouette: Transferring silhouette photo style for images

Silhouette photos are attractive visual artworks. We propose a photo style transfer approach that can generate realistic silhouette images. The motivation for this approach is to help ordinary users to gain silhouette photos which are difficult to shoot because of the limitations of cameras, weather and skills. Our approach consists of two major steps. The first step is an automatic sky segmentation to divide an input image into an object foreground and a sky background. The core step is the second one, in which the colors of the foreground and background are tuned according to the characteristics of silhouette photos respectively. Specifically, a statistic color transfer method is applied to the sky background using a perfect sky photo taken at dusk or dawn, and an adaptive color correction approach is further presented for the foreground to ensure a harmonious aesthetic effect. Extensive experiments show that our method can achieve satisfactory sky segmentation results and generate aesthetically pleasing silhouette photos.

Emerging From Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer

Underwater vision suffers from severe effects due to selective attenuation and scattering when light propagates through water. Such degradation not only affects the quality of underwater images but limits the ability of vision tasks. Different from existing methods which either ignore the wavelength dependency of the attenuation or assume a specific spectral profile, we tackle color distortion problem of underwater image from a new view. In this letter, we propose a weakly supervised color transfer method to correct color distortion, which relaxes the need of paired underwater images for training and allows for the underwater images unknown where were taken. Inspired by Cycle-Consistent Adversarial Networks, we design a multi-term loss function including adversarial loss, cycle consistency loss, and SSIM (Structural Similarity Index Measure) loss, which allows the content and structure of the corrected result the same as the input, but the color as if the image was taken without the water. Experiments on underwater images captured under diverse scenes show that our method produces visually pleasing results, even outperforms the art-of-the-state methods. Besides, our method can improve the performance of vision tasks.

Texture-Aware Emotional Color Transfer Between Images

Different colors can evoke different emotions for images. For example, artists usually use different color combinations to express different emotions when creating posters or pictures. Existing color transfer methods can successfully transfer visual appearance between images by changing colors. However, they often fail to change an image to meet an accurate emotion. Nowadays, there are few emotional color transfer methods between images, and those methods are imperfect in transferring the emotion from one image to another image due to the inaccuracy in the emotion calculation of the reference image. Therefore, we propose a texture-aware emotional color transfer method between images, which can adjust an image with an emotion word or a reference image. Each emotion word represents a type of emotion, such as alluring, fresh, and antique. The user simply input an emotion word (e.g., lovely), the system can automatically adjust the image to the target emotion (e.g., fresh). First, we propose a new emotion calculation method to compute the target emotion from a reference image. Unlike previous methods, we take both texture features and main colors in our emotion calculation model. Then, in order to find the proper color combinations to reflect the target emotion, three color-emotion model databases are built by color numbers of the models. Those models are obtained by exploiting the famous art theories, and we design a novel strategy to select the most suitable color-emotion model from the databases. Finally, we propose a new color transfer algorithm by utilizing color adjustment and color blending to guarantee the color gradient and naturalness. Experiments show that our method’s results are more consistent with the emotion of the input image than the state-of-the-art algorithms.

多参数亮度值重映射的颜色传递方法

多参数亮度值重映射的颜色传递方法

Dark Image Enhancement Using Perceptual Color Transfer

In this paper, we introduce an image enhancing approach for transforming dark images into lightened scenes, and we evaluate such method in different perceptual color spaces, in order to find the best-suited for this particular task. Specifically, we use a classical color transfer method where we obtain first-order statistics from a target image and transfer them to a dark input, modifying its hue and brightness. Two aspects are particular to this paper, the application of color transfer on dark imagery and in the search for the best color space for the application. In this regard, the tests performed show an accurate transference of colors when using perceptual color spaces, being RLAB the best color space for the procedure. Our results show that the methodology presented in this paper can be a good alternative to low-light or night vision processing techniques. Besides, the proposed method has a low computational complexity, property that is important for real time applications or for low-resource systems. This method can be used as a preprocessing step in order to improve the recognition and interpretation of dark imagery in a wide range of applications.

ℒ0 Gradient‐Preserving Color Transfer

AbstractThis paper presents a new two‐step color transfer method which includes color mapping and detail preservation. To map source colors to target colors, which are from an image or palette, the proposed similarity‐preserving color mapping algorithm uses the similarities between pixel color and dominant colors as existing algorithms and emphasizes the similarities between source image pixel colors. Detail preservation is performed by an ℒ0 gradient‐preserving algorithm. It relaxes the large gradients of the sparse pixels along color region boundaries and preserves the small gradients of pixels within color regions. The proposed method preserves source image color similarity and image details well. Extensive experiments demonstrate that the proposed approach has achieved a state‐of‐art visual performance.

Sufficient Image Appearance Transfer Combining Color and Texture

Traditional color transfer methods can achieve satisfactory results for transferring the color style from a reference image to a source image, provided that the source image shares the similar color mood with the reference image. However, color transfer solutions are always sensitive to color category, which cannot generate natural results when the contents of the reference image and the source image are different, e.g., a lush tree in the reference image and a bare tree in the source image. In this situation, it is insufficient only through color transfer to transfer the appearance from the reference image to the source image only through color transfer, since other information such as texture should also be considered. To obtain sufficient appearance transfer results, we propose a new image appearance transfer method combining both color and texture features. Given a source image and a reference image, our method starts with feature detection and matching between the source image and the reference image. Then, we design a new method for expanding feature point sets to get texture transfer mark (TTM) and color transfer mark (CTM). TTM and CTM will guide texture transfer and color transfer, respectively. We demonstrate our appearance transfer algorithm between quantities of images and compare with results of existing methods. Experiment results show that given only a single reference image, our approach can produce more sufficient appearance transfer results than the state-of-the-art algorithms.

Cartoon and Texture Decomposition-Based Color Transfer for Fabric Images

A color design process for fabric images can resort to a solution of a color transfer problem based on given color themes. Usually, the color transfer process contains an image segmentation phase and an image construction phase. In this paper, a novel color transfer method for fabric images is proposed. Compared with classical color transfer methods, the new method has the following three main innovations. First, the new method, in its image segmentation phase, follows an assumption that a fabric image can be decomposed into cartoon and texture components, which means the new color transfer method, in its image segmentation, phase incorporates an image decomposition process. The advantage of the innovation is that the cartoon component is more suitable than the original image to be used to partition the fabric image. Second, the new color transfer method can generate more vivid color transfer results since the above texture component is used to describe yarn texture details in the image construction phase. Third, the total generalized variation (TGV) regularizer is used to further improve the performance of image decomposition. Here, the TGV regularizer is good at estimating the weak lightness variation of the cartoon component with the CIELab color scheme. In addition, by using the augmented Lagrange multiplier method, we derive an efficient algorithm to search for the solutions to the proposed color transfer problem. Numerical results demonstrate that the proposed color transfer method can generate better results for fabric images.

色彩のグラデーション分布に基づく色変換法とその感性評価

色彩のグラデーション分布に基づく色変換法とその感性評価