Attention based deep convolutional U-Net with CSA optimization for hyperspectral image denoising

Abstract

Hyperspectral image (HSI) de-noising plays a significant role in HSI quality enhancement because it consists of rich image information. Although, large amounts of information commonly have a lot of noise which will greatly impact HSI processing and application. Deep convolution neural networks (CNNs) have been predominant in image de-noising because of recent advances in deep learning. However, CNNs may result in performance degradation due to plain network depth growth. It leads to face challenges in the shallow image feature fusion and limits the potential of the network to extract HSI features. To overcome these problems, an attention-based deep convolutional U-Net is proposed in this research work. It improves the down-sampling and up-sampling strategies to extract the image features and restore the HSI information, respectively. In order to provide better de-noising performance, the edge information of the image is extracted with the aid of a depthwise and polarized self-attention mechanism. Moreover, the clonal selection optimization algorithm (CSOA) minimizes the loss function obtained from the proposed system by selecting optimal parameters. The dataset used to evaluate the proposed model is the Real-World Hyperspectral Images database and is implemented in the working platform of Python. The experimental result shows that the proposed system performs better than existing state-of-the-art de-noising schemes. The achieved PSNR, SSIM, SAM and ERGAS performance metrics values are: 35.82, 0.94, 3.14 and 19.42, respectively.