Intelligent Fusion of Infrared and Visible Image Data Based on Convolutional Sparse Representation and Improved Pulse-Coupled Neural Network

Multi-source information can be obtained through the fusion of infrared images and visible light images,which have the characteristics of complementary information.However,the existing acquisition methods of fusion images have disadvantages such as blurred edges,low contrast,and loss of details.Based on convolution sparse representation and improved pulse-coupled neural network this paper proposes an image fusion algorithm that decompose the source images into high-frequency and low-frequency subbands by non-subsampled Shearlet Transform(NSST).Furthermore,the low-frequency subbands were fused by convolutional sparse representation(CSR),and the high-frequency subbands were fused by an improved pulse coupled neural network(IPCNN)algorithm,which can effectively solve the problem of difficulty in setting parameters of the traditional PCNN algorithm,improving the performance of sparse representation with details injection.The result reveals that the proposed method in this paper has more advantages than the existing mainstream fusion algorithms in terms of visual effects and objective indicators.

Simultaneous denoising and resolution enhancement of seismic data based on elastic convolution dictionary learning

Enhancing seismic resolution is a key component in seismic data processing, which plays a valuable role in raising the prospecting accuracy of oil reservoirs. However, in noisy situations, existing resolution enhancement methods are difficult to yield satisfactory processing outcomes for reservoir characterization. To solve this problem, we develop a new approach for simultaneous denoising and resolution enhancement of seismic data based on convolution dictionary learning. First, an elastic convolution dictionary learning algorithm is presented to efficiently learn a convolution dictionary with stronger representation capability from the noisy data to be processed. Specifically, the algorithm introduces the elastic L1/2 norm as a sparsity constraint and employs a steepest gradient descent strategy to efficiently solve the frequency-domain linear system with substantial computational cost in a half-quadratic splitting framework. Then, based on the learned convolution dictionary, a weighted convolutional sparse representation paradigm is designed to encode the noisy data to acquire an optimal sparse approximation of the effective signal. Subsequently, a high-resolution dictionary with a broadband spectrum is constructed by the proposed parameter scaling strategy and matched filtering technique on the basis of atomic spectrum modeling. Finally, the optimal sparse approximation of the effective signal and the constructed high-resolution dictionary are used for data reconstruction to obtain the seismic signal with high resolution and high signal-to-noise ratio. Synthetic and field dataset examples are executed to check the effectiveness and reliability of the developed method. The results indicate that this method has a more competitive performance in seismic applications compared with the conventional deconvolution and spectral whitening methods.

A multi-source image fusion algorithm based on gradient regularized convolution sparse representation

Image fusion based on the sparse representation(SR)has become the primary research direction of the transform domain method.However,the SR-based image fusion algorithm has the characteristics of high computational complexity and neglecting the local features of an image,resulting in limited image detail retention and a high registration misalignment sensitivity.In order to overcome these shortcomings and the noise existing in the image of the fusion process,this paper proposes a new signal decomposition model,namely the multi-source image fusion algorithm of the gradient regularization convolution SR(CSR).The main innovation of this work is using the sparse optimization function to perform two-scale decomposition of the source image to obtain high-frequency components and low-frequency components.The sparse coefficient is obtained by the gradient regularization CSR model,and the sparse coefficient is taken as the maximum value to get the optimal high frequency component of the fused image.The best low frequency component is obtained by using the fusion strategy of the extreme or the average value.The final fused image is obtained by adding two optimal components.Experimental results demonstrate that this method greatly improves the ability to maintain image details and reduces image registration sensitivity.

An Improved Pigeon-Inspired Optimization for Multi-focus Noisy Image Fusion

Image fusion technology is the basis of computer vision task,but information is easily affected by noise during transmission.In this paper,an Improved Pigeon-Inspired Optimization(IPIO)is proposed,and used for multi-focus noisy image fusion by combining with the boundary handling of the convolutional sparse representation.By two-scale image decomposition,the input image is decomposed into base layer and detail layer.For the base layer,IPIO algorithm is used to obtain the optimized weights for fusion,whose value range is gained by fusing the edge information.Besides,the global information entropy is used as the fitness index of the IPIO,which has high efficiency especially for discrete optimization problems.For the detail layer,the fusion of its coefficients is completed by performing boundary processing when solving the convolution sparse representation in the frequency domain.The sum of the above base and detail layers is as the final fused image.Experimental results show that the proposed algorithm has a better fusion effect compared with the recent algorithms.