Due to the problems of long iteration time and poor image quality in the traditional infrared multispectral image reconstruction method based on compressed sensing(CS), an auto-encoders network based on residuals is proposed. Autoencoders are unsupervised neural networks where the output and input layers share the same number of nodes, and which can reconstruct its own inputs through encoder and decoder functions. using code decoding technique learn from real infrared multispectral image spectrum information, through the fast image reconstruction of auto-encoder, get high quality image. The performance of the method is verified by using multiple infrared multispectral images. The results show that the method has the advantages of high image processing efficiency and high spatial resolution. Compared with the traditional compressed sensing method, the auto-encoder network based on residuals has better effect on infrared multispectral image reconstruction.
In order to compensate for the low spatial resolution of laser illumination imaging system due to the single photon detector with small number of pixels. In order to solve this problem, we demonstrated a laser illumination imaging system with compressed coded and introduced the application of deep learning in compressed sensing (CS) image reconstruction based on residual network. Specifically, by considering the priori information of sparsity, the better imaging results with much higher resolution could be obtained with a small amount of observation data. The digital micro-mirror device (DMD) is used to achieve sparse coding in this work. We designed to use two detectors to collect information in two reflection directions of DMD, which can reduce samples by 50%. In addition, considering that the time complexity of traditional CS reconstruction methods is too high, so we introduced CS reconstruction method based on residual network into our work, and did the simulation experiments with our data. According to the experimental results, our method performed better at the perspective of image quality evaluation index PSNR and consumption time in reconstruction process.
To reduce the influence of noise in infrared spectral signal measurement, a topological derivative improved partial differential equation method for infrared spectral data denoising is proposed. As an indicator function, topological derivative through a minimization process to find the best position to introduce disturbance, where are spectral edge points, then select the most excellent diffusion coefficient, so the cost of the minimum functional value. Based on the idea of topological optimization, it makes the lowest topological derivative to be optimum one. Then the diffusion is applied by using partial differential equation. Several simulated infrared spectral sequences are utilized to verify the performance of the proposed method. The experiment results show that our method is better in denoising.
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