Complementary Color Wavelet: A Novel Tool for the Color Image/Video Analysis and Processing

Yang, Chen; Li, Dan

doi:10.1109/tcsvt.2017.2776239

Cited by 16 publications

(18 citation statements)

References 38 publications

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“…Such approaches ignore the mutual information between the three channels. CCW not only retains the chroma characteristics of each channel of the color image, but also further considers the information among the channels, thus providing a more effective means for chroma detection [15].…”

Section: Complementary Color Wavelet Chroma Detection Operatormentioning

confidence: 99%

“…To achieve a more comprehensive examination of the existence of stitching tampering in digital images, a multi-feature tampering detection algorithm combining chroma, sharpness, and natural scene statistics (NSS) is proposed in this paper. The color filtering feature of complementary color wavelet (CCW) [15] is used to obtain the chromatic change of the image. Sharpness detection is then applied to detect the change in the sharpness of the image.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Feature Extraction-Based Algorithm for Stitching Tampered/Untampered Image Classification

Jia

Nahli

et al. 2022

Applied Sciences

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With the recent internet connectivity revolution, and the fast-growing prevalence of camera-enabled devices, images play a vital role in several fields of modern life. Photos, which often have been seen as evidence in courts, are nowadays subject to more sophisticated tricky forgery. To detect the image stitching between originally unassociated people/scenes and other combining forgery, an algorithm used to extract multiple specific image features, such as grayscale, complementary color wavelet (CCW) based chroma, sharpness, and natural scene statistics (NSS), is first presented in this paper. It is illustrated that a random forest model can be trained by these extracted features and then be employed to classify the stitching tampered/untampered images. The experimental results show that the proposed algorithm favorably outperforms the techniques reported in the literature, and achieves a state-of-the-art performance with higher accuracy values of 91%, 95.24%, and 88.02% on the Tampering ImageNet, Columbia, and CASIA ITDE V2.0 datasets, respectively. The precision, recall, and F1-score were also improved to a certain extent.

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Section: Complementary Color Wavelet Chroma Detection Operatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multi-Feature Extraction-Based Algorithm for Stitching Tampered/Untampered Image Classification

Jia

Nahli

et al. 2022

Applied Sciences

Self Cite

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“…Image enhancement-based dehazing methods focus mainly on the haze image contrast, edge gradient, and other information. e most common dehazing methods include wavelet transform [1], Retinex method [2], and histogram equalization [3]. Jobson et al [4] proposed a defogging method for the Retinex image enhancement algorithm.…”

Section: Introductionmentioning

confidence: 99%

DefogNet: A Single‐Image Dehazing Algorithm with Cyclic Structure and Cross‐Layer Connections

et al. 2021

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Inspired by the application of CycleGAN networks to the image style conversion problem Zhu et al. (2017), this paper proposes an end-to-end network, DefogNet, for solving the single-image dehazing problem, treating the image dehazing problem as a style conversion problem from a fogged image to a nonfogged image, without the need to estimate a priori information from an atmospheric scattering model. DefogNet improves on CycleGAN by adding a cross-layer connection structure in the generator to enhance the network’s multiscale feature extraction capability. The loss function was redesigned to add detail perception loss and color perception loss to improve the quality of texture information recovery and produce better fog-free images. In this paper, the novel Defog-SN algorithm is presented. This algorithm adds a spectral normalization layer to the discriminator’s convolution layer to make the discriminant network conform to a 1-Lipschitz continuum and further improve the model’s stability. In this study, the experimental process is completed based on the O-HAZE, I-HAZE, and RESIDE datasets. The dehazing results show that the method outperforms traditional methods in terms of PSNR and SSIM on synthetic datasets and Avegrad and Entropy on naturalistic images.

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“…The analytic Gabor filters were used to analyze the color texture by calculating the first-and second-order statistics of the filtered response and using homomorphic filtering to compensate for illumination changes in the image [35]. In order to better describe the color, Chen et al [36] designed a new color image processing tool called Complementary Color Wavelet Transform (CCWT) to make up for the gap between wavelet and complementary color. A more comprehensive study of color texture analysis can be found in [37], [38].…”

Section: Introductionmentioning

confidence: 99%

Completed Extremely Nonnegative DMD for Color Texture Classification

et al. 2020

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Dense micro-block difference (DMD) has achieved good performance in gray texture representation and classification. However, its performance is not satisfactory when representing color texture. To alleviate this problem, we propose a novel color texture representation method based on Completed Extremely Nonnegative DMD (CEN-DMD) in this paper. In particular, we first use DMD to model interchannel features and interchannel features of color texture images. Considering that negative value is meaningless in a digital image, we perform a nonnegative operation during the difference process. Due to that the maximum value in a nonnegative difference patch represents a significant difference, we construct the Extremely Nonnegative DMD (EN-DMD) by fusing the maximum values of the intrachannel features and the maximum of interchannel features, and further build Completed Extremely Nonnegative DMD (CEN-DMD) by fusing EN-DMDs at five scales and the global feature of the color texture images. Finally, the Fisher Vector is used to encode the CEN-DMD to obtain a color texture descriptor. Experimental results on five published standard color texture datasets (CUReT, Colored Brodatz, VisTex, USPTex and KTH-TIPS) reveal that CEN-DMD is effective when compared to the thirteen representative color texture classification methods. INDEX TERMS color texture representation, dense micro-block difference (DMD), extremely nonnegative multiresolution, texture classification

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Complementary Color Wavelet: A Novel Tool for the Color Image/Video Analysis and Processing

Cited by 16 publications

References 38 publications

A Multi-Feature Extraction-Based Algorithm for Stitching Tampered/Untampered Image Classification

A Multi-Feature Extraction-Based Algorithm for Stitching Tampered/Untampered Image Classification

DefogNet: A Single‐Image Dehazing Algorithm with Cyclic Structure and Cross‐Layer Connections

Completed Extremely Nonnegative DMD for Color Texture Classification

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