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.

Dual-Channel Densenet for Hyperspectral Image Classification

Abstract: Deep neural networks provide deep extracted features for image classification. As a high dimension data, hyperspectral image (HSI) feature extraction is unlike an RGB image whose feature representation could not be simply generated in the spatial domain. To take full advantage of HSI, a dual-channel convolutional neural network (CNN) is applied, 1D convolution for the spectral domain and 2D convolution for spatial domain. For pixel-wise classification of HSI, in our network model, one-dimensional customized DenseNet is for extracting the hierarchical spectral features and another customized DenseNet is applied to extract the hierarchical spatial-related feature. Furthermore, we experimentally tuned the several widen factors and dense-net growth rates to evaluate the impact of hyper-parameter. To compare our proposed method with HSI classification methods, we test other three DNNs based method in two real-world HSI dataset. The result demonstrated our approach outperformed the state-of-art method.

Ground Moving Target Imaging Based on Compressive Sensing Framework With Single-Channel SAR

Abstract: Ground moving target imaging (GMTIm) is considered one of the most important applications of synthetic aperture radar (SAR). Phase modulation from a moving target’s higher- order movements severely degrades the focusing quality of SAR images, because the conventional SAR-GMTIm algorithm assumes a constant target velocity in high-resolution GMTIm with single channel SAR. To solve this problem, a novel SAR-GMTIm algorithm in the compressive sensing (CS) framework is proposed to obtain high-resolution SAR images with highly focused responses and accurate re-location. After taking direct action on data in the defocused region of interest (ROI) from the entire image scene, CS theory is used to decompose an SAR-GMTIm signal into a set of the polynomial basis functions to remove the various phase errors related to higher- order movements. A modified orthogonal matching pursuit (MOMP)-type basis function-searching scheme is adopted to determine the motion parameter and reconstruct the sensing dictionary matrix. We can generate a refocused image of SAR-GMTIm from the complete SAR-GMTIm signal recovered using the proposed method. Finally, simulated and real measured SAR data are used to validate the effectiveness and superiority of the proposed method for SAR-GMTIm.

Underwater image enhancement method based on adaptive attenuation-curve prior.

Abstract: The attenuation (sum of absorption and scattering), which is caused by the dense and non-uniform medium, generally leads to problems of color degradation and detail loss in underwater imaging. In this study, we describe an underwater image enhancement method based on adaptive attenuation-curve prior. This method uses color channel transfer (CCT) to preprocess the underwater images, light smoothing, and wavelength-dependent attenuation to estimate water light and obtain the attenuation ratio between color channels, and estimates and refines the initial relative transmission of the channel. Additionally, the method calculates the attenuation factor and saturation constraints of the three color channels and generates an adjusted reverse saturation map (ARSM) to address uneven light intensity, after which the image is restored through water light and transmission estimation. Furthermore, we applied white balance fusion globally guided image filtering (G-GIF) technology to achieve color enhancement and edge detail preservation in the underwater images. Comparison experiments showed that the proposed method obtained better color and de-hazing effects, as well as clearer edge details, relative to current methods.