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.

Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism

Abstract: In recent years, image classification on hyperspectral imagery utilizing deep learning algorithms has attained good results. Thus, spurred by that finding and to further improve the deep learning classification accuracy, we propose a multi-scale residual convolutional neural network model fused with an efficient channel attention network (MRA-NET) that is appropriate for hyperspectral image classification. The suggested technique comprises a multi-staged architecture, where initially the spectral information of the hyperspectral image is reduced into a two-dimensional tensor, utilizing a principal component analysis (PCA) scheme. Then, the constructed low-dimensional image is input to our proposed ECA-NET deep network, which exploits the advantages of its core components, i.e., multi-scale residual structure and attention mechanisms. We evaluate the performance of the proposed MRA-NET on three public available hyperspectral datasets and demonstrate that, overall, the classification accuracy of our method is 99.82 %, 99.81%, and 99.37, respectively, which is higher compared to the corresponding accuracy of current networks such as 3D convolutional neural network (CNN), three-dimensional residual convolution structure (RES-3D-CNN), and space–spectrum joint deep network (SSRN).

Pseudo four-channel image denoising for noisy CFA raw data

Abstract: Most demosaicking algorithms only focus on handling noise-free CFA raw data. In practice, the CFA raw data are corrupted by noise, which degrades demosaicking performance. Full-color image quality strongly depends on the performance of the demosaicking. Here, we propose a CFA raw data denoising algorithm. In the proposed algorithm, the CFA raw data is converted to a pseudo four-channel image by rearranging pixels. Then, the four-channel data are transformed based on the principal component analysis (PCA). Existing high-performance gray image denoising algorithm is applied to each transformed image. Finally, the denoised data is rearranged to obtain denoised CFA raw data. We evaluate both the denoised CFA raw data as well as the full-color image reconstructed with the noisy CFA raw data. Experimental comparisons demonstrate that the proposed algorithm outperforms existing state-of-the-art algorithms.

Scene-aware image dehazing based on sky-segmented dark channel prior

Abstract: An improved dehazing algorithm based on dark channel theory is proposed, in order to solve the problems of colour distortion and halo effect which still exists in dark channel prior algorithm. The dark channel prior theory may lead to colour distortion in sky region. Firstly, the guided filter is used to refine the segmentation of the sky region, and the atmospheric light is estimated accurately. Then, the median filter is used to obtain the detailed edge information. So a more clear transmission can be gotten which effectively suppress the halo problem. Finally, the gamma correction is applied to enhance image lightness with an empirically selected gamma parameter. The experimental results show that the proposed algorithm can effectively remove the haze. It can correct the colour distortion of the sky area and eliminate the halo effect at the edge of the scene.

Methods and apparatus for adaptive foreground background analysis

Abstract: Apparatus and methods for target detection in hyperspectral images are disclosed. In one embodiment, a method of detecting a target in a hyperspectral image includes spectrally unmixing the hyperspectral image into segments, each segment having at least one of similar spectral composition, similar textural composition, and similar variation, and spectrally unmixing at least one of the segments. The method further includes creating a clutter rejection filter for at least one segment, filtering at least one segment, and calculating target abundances in at least one segment. In alternate embodiments, channel reduction can be performed on the hyperspectral image and also on at least one segment. In further embodiments, data associated with the location of possible targets in the segments may be compiled. In yet another embodiment, this data may be compressed by cross referencing data from all segments and eliminating redundancies.