Hierarchical extreme learning machine based image denoising network for visual Internet of Things

In the visual Internet of Things (VIoT), imaging sensors must achieve a balance between limited bandwidth and useful information when images contain heavy noise. In this paper, we address the problem of removing heavy noise and propose a novel hierarchical extreme learning machine-based image denoising network, which comprises a sparse auto-encoder and a supervised regression. Due to the fast training of a hierarchical extreme learning machine, an effective image denoising system that is robust for various noise levels can be trained more efficiently than other denoising methods, using a deep neural network. Our proposed framework also contains a non-local aggregation procedure that aims to fine-tune noise reduction according to structural similarity. Compared to the compression ratio in noisy images, the compression ratio of denoised images can be dramatically improved. Therefore, the method can achieve a low communication cost for data interactions in the VIoT. Experimental studies on images, including both hand-written digits and natural scenes, have demonstrated that the proposed technique achieves excellent performance in suppressing heavy noise. Further, it greatly reduces the training time, and outperforms other state-of-the-art approaches in terms of denoising indexes for the peak signal-to-noise ratio (PSNR) or the structural similarity index (SSIM).