Reconstruction of compressed spectral imaging based on global structure and spectral correlation
This work addresses the challenge of enhancing reconstruction quality in compressive spectral imaging, which is important for applications like remote sensing and medical imaging, though it appears incremental as it builds on existing convolutional sparse coding techniques.
The paper tackles the problem of reconstructing compressed spectral images by proposing a convolutional sparse coding method that incorporates global structure and spectral correlation, resulting in improvements of up to 4 dB in PSNR and 10% in SSIM compared to existing methods.
In this paper, a convolutional sparse coding method based on global structure characteristics and spectral correlation is proposed for the reconstruction of compressive spectral images. The spectral data is regarded as the convolution sum of the convolution kernel and the corresponding coefficients, using the convolution kernel operates the global image information, preserving the structure information of the spectral image in the spatial dimension. To take full exploration of the constraints between spectra, the coefficients corresponding to the convolution kernel are constrained by the L_(2,1)norm to improve spectral accuracy. And, to solve the problem that convolutional sparse coding is insensitive to low frequency, the global total-variation (TV) constraint is added to estimate the low-frequency components. It not only ensures the effective estimation of the low-frequency but also transforms the convolutional sparse coding into a de-noising process, which makes the reconstructing process simpler. Simulations show that compared with the current mainstream optimization methods, the proposed method can improve the reconstruction quality by up to 4 dB in PSNR and 10% in SSIM, and has a great improvement in the details of the reconstructed image.