Image Decolorization Research Articles

A dual-branch joint learning network for underwater object detection

Underwater object detection (UOD) is crucial for developing marine resources, environmental monitoring, and ecological protection. However, the degradation of underwater images limits the performance of object detectors. Most existing schemes treat underwater image enhancement (UIE) and UOD as two independent tasks, which take UIE as a preprocessing step to reduce the degradation problem, thus being unable to improve the detection accuracy effectively. Therefore, in this paper, we propose a dual-branch joint learning network (DJL-Net) that combines image processing and object detection through multi-task joint learning to construct an end-to-end model for underwater detection. With the dual-branch structure, DJL-Net can use the enhanced images generated by the image-processing module to supplement the features lost due to the degradation of the original underwater images. Specifically, DJL-Net first employs an image decolorization module governed by the detection loss, generating gray images to eliminate color disturbances stemming from underwater light absorption and scattering effects. An improved edge enhancement module is utilized to enhance the shape and texture expression in gray images and improve the recognition of object boundary features. Then, the generated edge-enhanced gray images and their original underwater images are input into the two branches to learn different types of features. Finally, a tridimensional adaptive gated feature fusion module is proposed to effectively fuse the complementary features learned from the two branches. Comprehensive experiments on four UOD datasets, including some scenes with challenging underwater environments, demonstrate the effectiveness and robustness of the proposed DJL-Net.

An Efficient and Effective Image Decolorization Algorithm Based on Cumulative Distribution Function.

Image decolorization is an image pre-processing step which is widely used in image analysis, computer vision, and printing applications. The most commonly used methods give each color channel (e.g., the R component in RGB format, or the Y component of an image in CIE-XYZ format) a constant weight without considering image content. This approach is simple and fast, but it may cause significant information loss when images contain too many isoluminant colors. In this paper, we propose a new method which is not only efficient, but also can preserve a higher level of image contrast and detail than the traditional methods. It uses the information from the cumulative distribution function (CDF) of the information in each color channel to compute a weight for each pixel in each color channel. Then, these weights are used to combine the three color channels (red, green, and blue) to obtain the final grayscale value. The algorithm works in RGB color space directly without any color conversion. In order to evaluate the proposed algorithm objectively, two new metrics are also developed. Experimental results show that the proposed algorithm can run as efficiently as the traditional methods and obtain the best overall performance across four different metrics.

A multi-tasking novel variational model for image decolorization and denoising

Image decolorization has a wide range of applications in digital printing, photo rendering and single-channel image processing. During the process of image decolorization, it is significant to retain more contrast information and avoid increasing the noise level of the original image. As we know, noise was hardly considered in previous decolorization methods. In this paper, we propose a new multi-tasking variational model with two tasks: decolorizing and denoising. The first task is image decolorization, that is, to transform color images to grayscale images. The other task is to control the noise level, and even eliminate the noise. Inspired by TV-Stokes model, we introduce the unit normal vector containing the basic geometry information of the color image in the model. The existence and the uniqueness of the solution to the variational problems are proved. For the variational model, two numerical methods are adopted. At first, we use gradient descent method to obtain evolution equations and construct finite difference schemes to solve the corresponding equations of proposed variational model. Furthermore, an efficient augmented Lagrangian approach is applied to solve the multi-tasking variational model directly. For noisy images and clean images, we employ two output patterns of grayscale images respectively. Especially, our model is stable for the noisy images. Numerical results and comparisons show that the proposed model can produce highly competitive results in terms of decolorization quality of noisy images, edge preservation and adjustment of parameters.

Quaternion Chromaticity Contrast Preserving Decolorization Method Based on Adaptive Singular Value Weighting

Color image decolorization can not only simplify the complexity of image processing and analysis, improving computational efficiency, but also help to preserve the key information of the image, enhance visual effects, and meet various practical application requirements. However, with existing decolorization methods it is difficult to simultaneously maintain the local detail features and global smooth features of the image. To address this shortcoming, this paper utilizes singular value decomposition to obtain the hierarchical local features of the image and utilizes quaternion theory to overcome the limitation of existing color image processing methods that ignore the correlation between the three channels of the color image. Based on this, we propose a singular value adaptive weighted fusion quaternion chromaticity contrast preserving decolorization method. This method utilizes the low-rank matrix approximation principle to design a singular value adaptive weighted fusion strategy for the three channels of the color image and implements image decolorization based on singular value adaptive weighting. To address the deficiency of the decolorization result obtained in this step, which cannot maintain global smoothness characteristics well, a contrast preserving decolorization algorithm based on quaternion chromaticity distance is further proposed, and the global weighting strategy obtained by this algorithm is integrated into the image decolorization based on singular value adaptive weighting. The experimental results show that the decolorization method proposed in this paper achieves excellent results in both subjective visual perception and objective evaluation metrics.

Invertible Image Decolorization with CFEH and Reversible Data Hiding

Invertible Image Decolorization with CFEH and Reversible Data Hiding

No-Reference Quality Metrics for Image Decolorization

No-Reference Quality Metrics for Image Decolorization

Decolorization based on the weighted combination of image entropy and canny edge retention ratio

Image decolorization is used to realize the color-to-gray conversion, which is widely used in black-and-white printing, digital-ink display, photo stylization, etc. Image contrast fidelity preservation is the key issue during this conversion process. We present a parametric model combining image entropy and the Canny edge retention ratio (CERR). Maximizing the image entropy keeps the global image contrast content, while maximizing the CERR enhances the local image contrast, mainly along the edges. The weighting parameter λ can be robustly estimated. Experimental results on Cadik’s and the COLOR250 datasets demonstrate that the model can achieve significant performance improvement over state-of-the-art methods both qualitatively and quantitatively.

A New Image Decolorization Evaluation Quality Metric

Image decolorization, the process of color-to-gray conversion, plays a crucial role in single-channel processing, computer vision, digital printing, and monochrome visualization. This process induces new artifacts, the impact of which on visual quality has to be identified. While visual quality assessment has been the subject of many studies, there are still some open questions regarding new color-to-gray conversion quality metrics. For example, computer simulations show that the commonly used grayscale conversion quality metrics such as CCPR, CCFR, and E-score depend on parameters and may pick different best decolorization methods by changing the parameters. This paper proposes a new quality metric to evaluate image decolorization methods. It uses the human visual properties information and regression method. Experimental results also show (i) strong correlations between the presented image decolorization quality metric and the Mean Opinion Score (MOS), (ii) more robust than the existing quality metrics, and (iii) help to choose the best state-of-the-art decolorization methods using the presented metric and existing quality metrics.

Warm-cool color-based high-speed decolorization: an empirical approach for tone mapping applications

Grayscale images are fundamental to many image processing applications like data compression, feature extraction, printing and tone mapping. However, some image information is lost when converting from color to grayscale. In this paper, we propose a light-weight and high-speed image decolorization method based on human perception of color temperatures. Chromatic aberration results from differential refraction of light depending on its wavelength. It causes some rays corresponding to cooler colors (like blue, green) to converge before the warmer colors (like red, orange). This phenomena creates a perception of warm colors "advancing" toward the eye, while the cool colors to be "receding" away. In this proposed color to gray conversion model, we implement a weighted blending function to combine red (perceived warm) and blue (perceived cool) channel. Our main contribution is threefold: First, we implement a high-speed color processing method using exact pixel by pixel processing, and we report a $5.7\times$ speed up when compared to other new algorithms. Second, our optimal color conversion method produces luminance in images that are comparable to other state of the art methods which we quantified using the objective metrics (E-score and C2G-SSIM) and a subjective user study. Third, we demonstrate that an effective luminance distribution can be achieved using our algorithm by using global and local tone mapping applications.

Video Decolorization Based on the CNN and LSTM Neural Network

Video decolorization is the process of transferring three-channel color videos into single-channel grayscale videos, which is essentially the decolorization operation of video frames. Most existing video decolorization algorithms directly apply image decolorization methods to decolorize video frames. However, if we only take the single-frame decolorization result into account, it will inevitably cause temporal inconsistency and flicker phenomenon meaning that the same local content between continuous video frames may display different gray values. In addition, there are often similar local content features between video frames, which indicates redundant information. To solve the preceding problems, this article proposes a novel video decolorization algorithm based on the convolutional neural network and the long short-term memory neural network. First, we design a local semantic content encoder to learn and extract the same local content of continuous video frames, which can better preserve the contrast of video frames. Second, a temporal feature controller based on the bi-directional recurrent neural networks with Long short-term memory units is employed to refine the local semantic features, which can greatly maintain temporal consistency of the video sequence to eliminate the flicker phenomenon. Finally, we take advantages of deconvolution to decode the features to produce the grayscale video sequence. Experiments have indicated that our method can better preserve the local contrast of video frames and the temporal consistency over the state of the-art.

Smoothing and Clustering Guided Image Decolorization

In this paper, we propose a new image decolorization method based on image clustering and weight optimization. First, we smooth the color image and cluster it into several classes and get the class centers. Each center can represent a distinctive color in the image. Then the class centers are sorted according to their brightness measured by Euclidean norm. By assuming that the decolorized grayscale image is a linear combination of the three channels of the color image, we propose an optimization problem by forcing the sorted class centers to correspond to specified grayscale values satisfying uniform distribution. Numerically, the problem is solved by quadratic programming. Experiments on two popular data sets demonstrate that the proposed method is competitive with the state-of-the-art decolorization method.

Invertible Image Decolorization.

Invertible image decolorization is a useful color compression technique to reduce the cost in multimedia systems. Invertible decolorization aims to synthesize faithful grayscales from color images, which can be fully restored to the original color version. In this paper, we propose a novel color compression method to produce invertible grayscale images using invertible neural networks (INNs). Our key idea is to separate the color information from color images, and encode the color information into a set of Gaussian distributed latent variables via INNs. By this means, we force the color information lost in grayscale generation to be independent of the input color image. Therefore, the original color version can be efficiently recovered by randomly re-sampling a new set of Gaussian distributed variables, together with the synthetic grayscale, through the reverse mapping of INNs. To effectively learn the invertible grayscale, we introduce the wavelet transformation into a UNet-like INN architecture, and further present a quantization embedding to prevent the information omission in format conversion, which improves the generalizability of the framework in real-world scenarios. Extensive experiments on three widely used benchmarks demonstrate that the proposed method achieves a state-of-the-art performance in terms of both qualitative and quantitative results, which shows its superiority in multimedia communication and storage systems.

Image Decolorization Combining Local Features and Exposure Features

Image decolorization is a task aiming to transform a color image to a grayscale one and is a dimension reduction process which inevitably suffers from information loss. The general goal of image decolorization is to preserve the color contrast of the color image. According to human visual study, exposure affects the human visual perception, and low-exposure areas or over-exposure areas will first attract the sense of sight. In addition, exposure also affects the contrast of the image, the contrast of low-exposure areas and over-exposure areas often cannot be well shown. Thus, the exposure should be taken into account in the process of image decolorization. Traditional local methods are not accurate enough to process local pixel blocks which may tend to cause local artifacts, while traditional global methods cannot greatly deal with local color blocks, which are usually time consuming too. Besides, the traditional image decolorization method usually uses the low-level features of an image. In this paper, the convolutional neural network is used to learn high-level abstract features of the image. We design a new convolutional neural-network framework with a local feature network and a rough classifier, which can learn the local semantic features and distinguish the different exposure conditions of color images. It is possible to learn the mapping model between input–output image pairs, which can generate better results in terms of color contrast preservation and exposure adjustment. Experiments indicate that our method does better in terms of color contrast preservation and exposure adjustment than the state of the art.

Hazy Image Decolorization with Color Contrast Restoration.

It is challenging to convert a hazy color image into a gray-scale image because the color contrast field of a hazy image is distorted. In this paper, a novel decolorization algorithm is proposed to transfer a hazy image into a distortionrecovered gray-scale image. To recover the color contrast field, the relationship between the restored color contrast and its distorted input is presented in CIELab color space. Based on this restoration, a nonlinear optimization problem is formulated to construct the resultant gray-scale image. A new differentiable approximation solution is introduced to solve this problem with an extension of the Huber loss function. Experimental results show that the proposed algorithm effectively preserves the global luminance consistency while represents the original color contrast in gray-scales, which is very close to the corresponding ground truth gray-scale one.

Significance of processing chrominance information for scene classification: a review

The primary objective of this paper is to provide a detailed review of various works showing the role of processing chrominance information for color-to-grayscale conversion. The usefulness of perceptually improved color-to-grayscale converted images for scene classification is then studied as a part of this presented work. Various issues identified for the color-to-grayscale conversion and improved scene classification are presented in this paper. The review provided in this paper includes, review on existing feature extraction techniques for scene classification, various existing scene classification systems, different methods available in the literature for color-to-grayscale image conversion, benchmark datasets for scene classification and color-to-gray-scale image conversion, subjective evaluation and objective quality assessments for image decolorization. In the present work, a scene classification system is proposed using the pre-trained convolutional neural network and Support Vector Machines developed utilizing the grayscale images converted by the image decolorization methods. The experimental analysis on Oliva Torralba scene dataset shows that the color-to-grayscale image conversion technique has a positive impact on the performance of scene classification systems.

Decolorization-Based Hyperspectral Image Visualization

Image decolorization is known to be an effective way in transferring a color image into a gray one while well preserving the major information of all three bands. In this paper, a simple yet effective hyperspectral image visualization framework based on decolorization, named decolorization based hyperspectral visualization, is proposed, which enables us to fully exploit the benefits of decolorization technique. The proposed framework consists of the following two main steps. First, the hyperspectral image is partitioned into nine subsets of adjacent hyperspectral bands and the averaged band of each subset is calculated. Then, the dimension reduced image is further divided into three groups of adjacent bands, and the bands in each group are fused by using an image decolorization method. The main contribution of this paper is that the strong correlations in two different fields, i.e., image decolorization and hyperspectral image visualization, are first built. Experiments performed on several real hyperspectral data sets demonstrate that the proposed framework can obtain outstanding visualization performance in terms of both subjective and objective evaluations.

Contrast preserving image decolorization combining global features and local semantic features

Image decolorization known as the process to transform a color image to a grayscale one is widely used in single-channel image processing, black and white printing, etc. It is a dimension reduction process which inevitably suffers from information loss. The general goal of image decolorization is to preserve the color contrast of the color image. Traditional image decolorization methods are generally divided into local methods and global methods. However, local methods are not accurate enough to process local pixel blocks which may tend to cause local artifacts. While global methods cannot deal well in local color blocks, which are usually time-consuming, too. Therefore, this paper presents a way to combine the local semantic features and the global features. The traditional image decolorization method uses the low-level features of an image. Instead, in this paper, the convolution neural network is used to learn the global features and local semantic features of an image which can better preserve the contrast in both local color blocks and adjacent pixels of the color image. Finally, the global features and the local semantic features are combined to decolorize the image. Experiments indicate that our method outperforms the state of the arts in terms of contrast preservation.

Significance of perceptually relevant image decolorization for scene classification

A color image contains luminance and chrominance components representing the intensity and color information respectively. The objective of the work presented in this paper is to show the significance of incorporating the chrominance information for the task of scene classification. An improved color-to-grayscale image conversion algorithm by effectively incorporating the chrominance information is proposed using color-to-gay structure similarity index (C2G-SSIM) and singular value decomposition (SVD) to improve the perceptual quality of the converted grayscale images. The experimental result analysis based on the image quality assessment for image decolorization called C2G-SSIM and success rate (Cadik and COLOR250 datasets) shows that the proposed image decolorization technique performs better than 8 existing benchmark algorithms for image decolorization. In the second part of the paper, the effectiveness of incorporating the chrominance component in scene classification task is demonstrated using the deep belief network (DBN) based image classification system developed using dense scale invariant feature transform (SIFT) as features. The levels of chrominance information incorporated by the proposed image decolorization technique is confirmed by the improvement in the overall scene classification accuracy . Also, the overall scene classification performance is improved by the combination of models obtained using the proposed and the conventional decolorization methods.

A two-stage parametric subspace model for efficient contrast-preserving decolorization

The RGB2GRAY conversion model is the most popular and classical tool for image decolorization. A recent study showed that adapting the three weighting parameters in this first-order linear model with a discrete searching solver has a great potential in its conversion ability. In this paper, we present a two-step strategy to efficiently extend the parameter searching solver to a two-order multivariance polynomial model, as a sum of three subspaces. We show that the first subspace in the two-order model is the most important and the second one can be seen as a refinement. In the first stage of our model, the gradient correlation similarity (Gcs) measure is used on the first subspace to obtain an immediate grayed image. Then, Gcs is applied again to select the optimal result from the immediate grayed image plus the second subspace-induced candidate images. Experimental results show the advantages of the proposed approach in terms of quantitative evaluation, qualitative evaluation, and algorithm complexity.

Efficient image decolorization with a multimodal contrast-preserving measure

Decolorization is widely used in monotonic displaying, black-and-white printing, single-channel image processing, and many computer vision applications. The conversion of a color image into a grayscale one suffers from data loss. This paper presents a novel parallel decolorization method which effectively preserves not only the spatially local color contrast but also the dominant non-local color contrast. A new multimodal contrast-preserving measure with a multimodal Gaussian distribution is proposed to relax the constraint of color contrast. The dominant non-local color pair set is constructed by taking advantage of a linear bounding volume hierarchy while the local color pair set is produced by removing duplicate instances of local color pairs. The whole pipeline design is highly parallel, allowing for a real-time GPU-based implementation. Experimental results and comparisons show that the proposed method can produce plausible decolorized images. A number of single-channel image processing applications, including edge detection, image segmentation, color augmentation, and image stylization, are demonstrated to verify the feasibility of the proposed decolorization method.