A new quaternion convolutional attention denoising network known as DeQCANet for color random-valued impulse noise removal is proposed in this paper. First, a structural information extraction block based on a dilated convolution operation is designed to extract the structure and detail feature map. Subsequently, a quaternion convolutional neural network with a new quaternion map construction strategy is implemented to gain the color features across channels further. Finally, to integrate the global and local features, a feature enhancement block based on the attention mechanism is proposed to guide the network for impulse noise denoising. The experimental results demonstrate that the proposed denoising technique exhibits competitive performance compared to other well-known color image denoising methods.

To achieve multiple color images encryption, a secure double-color-image encryption algorithm is designed based on the quaternion multiple parameter discrete fractional angular transform (QMPDFrAT), a nonlinear operation and a plaintext-related joint permutation-diffusion mechanism. QMPDFrAT is first defined and then applied to encrypt multiple color images. In the designed algorithm, the low-frequency and high-frequency sub-bands of the three color components of each plaintext image are obtained by two-dimensional discrete wavelet transform. Then, the high-frequency sub-bands are further made sparse and the main features of these sub-bands are extracted by a Zigzag scan. Subsequently, all the low-frequency sub-bands and high-frequency fusion images are represented as three quaternion signals, which are modulated by the proposed QMPDFrAT with three quaternion random phase masks, respectively. The spherical transform, as a nonlinear operation, is followed to nonlinearly make the three transform results interact. For better security, a joint permutation-diffusion mechanism based on plaintext-related random pixel insertion is performed on the three intermediate outputs to yield the final encryption image. Compared with many similar color image compression-encryption schemes, the proposed algorithm can encrypt double-color-image with higher quality of image reconstruction. Numerical simulation results demonstrate that the proposed double-color-image encryption algorithm is feasibility and achieves high security.

Hyperspectral image denoising is crucial for accurate extraction of spectral information. However, current convolutional neural network (CNN)-based methods have inherent limitations, while Transformer- based methods suffer from high computational complexity when processing global contextual information. To address this problem, we designed a hybrid Mamba-CNN context interaction module and constructed a U-shaped hierarchical encoder-decoder network (MUNet). The network takes the pixel-scale as input to maximize the preservation of image information and employs state-space model (SSM)-based Mamba blocks to efficiently capture global semantic information, while using convolution to extract local features. This enhances the modeling of global and local features for better denoising. Extensive experiments on synthetic and real hyperspectral image (HSI) datasets showed that the proposed MUNet achieves better performance than other state-of-the-art techniques.

Impulse noise detection is important to the restoration of color images contaminated by impulse noise in switching vector median filters. To increase detection accuracy, an effective color-impulse detector is presented. A new color distance metric based on quaternion theory is proposed. The proposed color distance metric is used to calculate the local density of a color pixel. A hard thresholding strategy is used to determine whether a color pixel is corrupted by impulse noise or not (i.e., an outlier). The noisy pixels detected will be restored by a weighted vector median filter, while the noise-free pixels remain unchanged. The experimental comparisons show that the proposed algorithm can obtain lower false and miss detection rate, and produces better performance in terms of peak signal-to-noise ratio and feature similarity measures, compared to other well-known color image filtering methods.

Due to the tremendous volume taken by the 3D point-cloud models, knowing how to achieve the balance between a high compression ratio, a low distortion rate, and computing cost in point-cloud compression is a significant issue in the field of virtual reality (VR). Convolutional neural networks have been used in numerous point-cloud compression research approaches during the past few years in an effort to progress the research state. In this work, we have evaluated the effects of different network parameters, including neural network depth, stride, and activation function on point-cloud compression, resulting in an optimized convolutional neural network for compression. We first have analyzed earlier research on point-cloud compression based on convolutional neural networks before designing our own convolutional neural network. Then, we have modified our model parameters using the experimental data to further enhance the effect of point-cloud compression. Based on the experimental results, we have found that the neural network with the 4 layers and 2 strides parameter configuration using the Sigmoid activation function outperforms the default configuration by 208% in terms of the compression-distortion rate. The experimental results show that our findings are effective and universal and make a great contribution to the research of point-cloud compression using convolutional neural networks.