Channel (digital image)

About: Channel (digital image) is a research topic. Over the lifetime, 7211 publications have been published within this topic receiving 69974 citations.

Ray tube tracing method for predicting indoor channel characteristics map

Abstract: A novel adaptive ray tube tracing algorithm optimised for creating indoor radio channel characteristic maps is presented. The algorithm, based on the shooting-and-bouncing-ray with image method and an adaptive resolution control technique, shows a large improvement in computational efficiency compared to conventional methods.

A blind color image watermarking scheme with variable steps based on Schur decomposition

Abstract: The widespread application of the Internet makes the protection of image copyright face serious challenges. For resolving this problem, this paper designs a blind color digital image watermarking algorithm which meets the requirements of invisibility, security and robustness. The advantages of the proposed method include the following two points: 1) the proposed method uses Affine transformation with large key space to encrypt the watermark information; 2) Schur decomposition with low complexity is selected and performed on the matrix blocks in different color channels of the host image. In this proposed method, the watermark embedding and blind extraction are completed by quantizing the eigenvalues on the diagonal of the decomposed matrix with different quantization steps. Experimental results show that the proposed method not only has good invisibility, but also has high security and strong robustness.

Hybrid 2-D–3-D Deep Residual Attentional Network With Structure Tensor Constraints for Spectral Super-Resolution of RGB Images

Abstract: RGB image spectral super-resolution (SSR) is a challenging task due to its serious ill-posedness, which aims at recovering a hyperspectral image (HSI) from a corresponding RGB image In this article, we propose a novel hybrid 2-D–3-D deep residual attentional network (HDRAN) with structure tensor constraints, which can take fully advantage of the spatial–spectral context information in the reconstruction progress Previous works improve the SSR performance only through stacking more layers to catch local spatial correlation neglecting the differences and interdependences among features, especially band features; different from them, our novel method focuses on the context information utilization First, the proposed HDRAN consists of a 2D-RAN following by a 3D-RAN, where the 2D-RAN mainly focuses on extracting abundant spatial features, whereas the 3D-RAN mainly simulates the interband correlations Then, we introduce 2-D channel attention and 3-D band attention mechanisms into the 2D-RAN and 3D-RAN, respectively, to adaptively recalibrate channelwise and bandwise feature responses for enhancing context features Besides, since structure tensor represents structure and spatial information, we apply structure tensor constraint to further reconstruct more accurate high-frequency details during the training process Experimental results demonstrate that our proposed method achieves the state-of-the-art performance in terms of mean relative absolute error (MRAE) and root mean square error (RMSE) on both the “clean” and “real world” tracks in the NTIRE 2018 Spectral Reconstruction Challenge As for competitive ranking metric MRAE, our method separately achieves a 1606% and 290% relative reduction on two tracks over the first place Furthermore, we investigate HDRAN on the other two HSI benchmarks noted as the CAVE and Harvard data sets, also demonstrating better results than state-of-the-art methods

Full-color structured illumination optical sectioning microscopy

Abstract: In merits of super-resolved resolution and fast speed of three-dimensional (3D) optical sectioning capability, structured illumination microscopy (SIM) has found variety of applications in biomedical imaging. So far, most SIM systems use monochrome CCD or CMOS cameras to acquire images and discard the natural color information of the specimens. Although multicolor integration scheme are employed, multiple excitation sources and detectors are required and the spectral information is limited to a few of wavelengths. Here, we report a new method for full-color SIM with a color digital camera. A data processing algorithm based on HSV (Hue, Saturation, and Value) color space is proposed, in which the recorded color raw images are processed in the Hue, Saturation, Value color channels, and then reconstructed to a 3D image with full color. We demonstrated some 3D optical sectioning results on samples such as mixed pollen grains, insects, micro-chips and the surface of coins. The presented technique is applicable to some circumstance where color information plays crucial roles, such as in materials science and surface morphology.

Hair Enhancement in Dermoscopic Images Using Dual-Channel Quaternion Tubularness Filters and MRF-Based Multilabel Optimization

Abstract: Hair occlusion is one of the main challenges facing automatic lesion segmentation and feature extraction for skin cancer applications. We propose a novel method for simultaneously enhancing both light and dark hairs with variable widths, from dermoscopic images, without the prior knowledge of the hair color. We measure hair tubularness using a quaternion color curvature filter. We extract optimal hair features (tubularness, scale, and orientation) using Markov random field theory and multilabel optimization. We also develop a novel dual-channel matched filter to enhance hair pixels in the dermoscopic images while suppressing irrelevant skin pixels. We evaluate the hair enhancement capabilities of our method on hair-occluded images generated via our new hair simulation algorithm. Since hair enhancement is an intermediate step in a computer-aided diagnosis system for analyzing dermoscopic images, we validate our method and compare it to other methods by studying its effect on: 1) hair segmentation accuracy; 2) image inpainting quality; and 3) image classification accuracy. The validation results on 40 real clinical dermoscopic images and 94 synthetic data demonstrate that our approach outperforms competing hair enhancement methods.