A multimodal image fusion method based on the joint sparse model (JSM), multiscale dictionary learning, and a structural similarity index (SSIM) is presented. As an effective signal representation technique, JSM is derived from distributed compressed sensing and has been successfully employed in many image-processing applications such as image classification and fusion. The highly redundant single dictionary always has difficulty satisfying the correlations between images in traditional JSM-based image fusion. Therefore, the proposed fusion model learns a more compact multiscale dictionary to effectively combine the multiscale analysis used in nonsubsampled contourlet transformation with the single-scale joint sparse representation used in image domains to solve the issues of single-scale sparse fusion and to improve fusion quality. The experimental results demonstrate that the proposed fusion method obtains the state-of-the-art performances in terms of both subjective visual quality and objective metrics, especially when fusing multimodal images.

A method for geometric distortion correction and space and time-varying blur reduction is proposed, which can recover the high-quality image from a single-frame image distorted by atmospheric turbulence. First, the U-net like deep-stacked autoencoder neural network model is proposed, which is composed of two deep convolutional autoencoder (CAE) neural networks and a U-net. The first CAE is used for feature extraction, the U-net is used for feature deconvolution, and the second CAE is used for image reconstruction. For the loss reduction of image information, transposed convolution instead of upsampling is selected in U-net networks. Moreover, in order to obtain sufficient feature information for reconstruction, the first CAE and the last CAE are symmetric skip connected. This not only enables the fusion of low-level and high-level information but also ensures the integrity of image information greatly. Then, a method of gradually mature training from simple to complex is proposed to overcome the difficulty of convergence on smaller training sets. It makes the network be converged and mature by increasing the complexity of training data gradually so as to restore the high turbulence-degraded image. Experimental results of actual observation data and simulation data show that the algorithm has a stronger antinoise ability and can recover image details and sharpen image edges more effectively. In particular, for atmospheric turbulence severely degraded image restoration, the peak signal-to-noise ratio index is increased by about 10% on average compared with state-of-the-art methods.