.png){kind=icon}
返回
Hyperspectral Image Compression Using Sampling and Implicit Neural Representations
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2024.3509718.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Hyperspectral images record the electromagnetic spectrum, and each hyperspectral pixel often stores hundreds of channels. Consequently, a hyperspectral image contains an order of magnitude more information than a similar-sized RGB color image. Concomitant with the decreasing cost of capturing these images, there is a need to develop efficient techniques for storing, transmitting, and analyzing hyperspectral images. This article develops a method for hyperspectral image compression using implicit neural representations (INRs) where a multilayer perceptron (MLP) network with sinusoidal activation functions learns to map pixel locations to pixel spectrum for a given hyperspectral image. This representation, thus, acts as a compressed encoding of this image, and the original image is reconstructed by evaluating this network at each pixel location. We introduce a sampling scheme to achieve better compression times while keeping decoding errors low. The proposed method is evaluated on four benchmarks against 16 other schemes for hyperspectral compression, and according to the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) metrics, the method developed in this article achieves state-of-the-art compression rates at low-bit rates. In addition, we show that the proposed sampling technique reduces encoding times.
Keyword:
Image coding
Hyperspectral imaging
Transform coding
Neural networks
Image reconstruction
Principal component analysis
Color
Gray-scale
Three-dimensional displays
Tensors
Hyperspectral image compression
implicit neural representations (INRs)
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
机构
暂无机构信息
引用论文
.png){kind=icon}
Advances in Hyperspectral Image and Signal Processing A comprehensive overview of the state of the art高光谱图像和信号处理的进展综述
PREAI
分享收藏
查看更多内容