In this paper, a method combining an error hiding algorithm with image super-resolution reconstruction is proposed, which uses packet loss compensation as an alternative to traditional reconstruction processes. Our algorithm provides an alternative to traditional frameworks by firstly estimating the edge direction of the block and then interpolating along the edge direction. We define a pixel span function to obtain the missing image details, and the pixels are reconstructed using this function to detect their possible textures. Experiments using established image data sets show that comparison with seven other classical image interpolation algorithms, the proposed approach achieves both higher quantitative and qualitative performance results, ultimately providing better visual effects.

Image interpolation model based on packet losing network

Most existing multi-focus color image fusion methods based on multi-scale decomposition consider three color components separately during fusion, which leads to inherent color structures change, and causes tonal distortion and blur in the fusion results. In order to address these problems, a novel fusion algorithm based on the quaternion multi-scale singular value decomposition (QMSVD) is proposed in this paper. First, the multi-focus color images, which represented by quaternion, to be fused is decomposed by multichannel QMSVD, and the low-frequency sub-image represented by one channel and high-frequency sub-image represented by multiple channels are obtained. Second, the activity level and matching level are exploited in the focus decision mapping of the low-frequency sub-image fusion, with the former calculated by using local window energy and the latter measured by the color difference between color pixels expressed by a quaternion. Third, the fusion results of low-frequency coefficients are incorporated into the fusion of high-frequency sub-images, and a local contrast fusion rule based on the integration of high-frequency and low-frequency regions is proposed. Finally, the fused images are reconstructed employing inverse transform of the QMSVD. Simulation results show that image fusion using this method achieves great overall visual effects, with high resolution images, rich colors, and low information loss.

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Multi-Focus Color Image Fusion Based on Quaternion Multi-Scale Singular Value Decomposition.

In the imaging process of bistatic inverse synthetic aperture radar (Bi–ISAR), the echo is easily affected by the internal interference and external environment of the radar system, resulting in the problems of sparse aperture and low echo signal noise ratio. The efficiency of conventional sparse aperture imaging methods is greatly reduced. To solve the above problems, a Bi–ISAR sparse aperture imaging algorithm based on Complex Variational Modal Decomposition (CVMD) and wavelet threshold de–noising is proposed. Firstly, the Bi–ISAR sparse aperture echo signal model is established, the sparse basis matching the echo is constructed, the echo signal is decomposed into different intrinsic mode functions (IMF) by CVMD, and the IMFs belonging to the signal are separated by the energy relationship criterion. Then, after the IMFs is de–noising by the improved wavelet threshold de–noising algorithm, the original signal is synthesized, and the signal after de–noising is reconstructed by using the orthogonal matching pursuing algorithm. Simulation results show that the proposed algorithm can achieve Bi–ISAR sparse aperture high−resolution imaging under low signal noise ratio and has fine anti−noise performance.

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Research on Bi–ISAR Sparse Aperture High Resolution Imaging Algorithm under Low SNR

To augment edge preservation capabilities and detail enhancement in multi-focus image fusion, a new Multi-focus image fusion algorithm is proposed in this paper. Firstly, we select fast local laplacian filtering (FLLF) as the multi-scale decomposition tool to decompose the source image into an approximate image and multiple detail images. Secondly, higher-order singular value decomposition (HOSVD) is applied to fuse the approximate image. Thirdly, the parameter-adaptive pulse coupled-neural network (PA-PCNN) model is used to fuse the detailed images. Finally, exploiting the combination of the fusion results of the approximate image and the detail images, the final fusion image is obtained using the FLLF inverse transform. The experimental results show that the proposed method achieves excellent performance in both subjective visual perception and objective index evaluation. 

Multi-focus image fusion based on HOSVD and parameter adaptive PCNN in fast local Laplacian filtering domain

Because images contain rich characteristic information, adaptive image decomposition algorithms are necessary to achieve multi-scale extraction of image information in multi-scale image decomposition processing. For this reason, based on local mean decomposition (LMD), which has good self-adaptive characteristics, this paper proposes a new adaptive image processing algorithm, bi-dimensional local mean decomposition (BLMD). BLMD can decompose the original image into multiple bi-dimensional product functions (BPFs). Aiming at the decomposition of BLMD, this paper proposes targeted solutions and designs for the extraction of extremum points, screening process interpolation methods, and decomposition and stop conditions involved in BLMD. After fully recognizing the self-adaptive and multi-scale characteristics of BLMD, this paper proposes a variable neighborhood window method to obtain the extreme points in the decomposition process and uses fractal theory to interpolate the image and obtain the corresponding mean surface and other information. Then, the number of non-coincident extreme points on the zero-valued plane projection between adjacent surfaces in the screening process is counted and analyzed, and a stop condition that matches the characteristics of the image is given to ensure the BPF component obtained by decomposition accurately reflects certain feature information of an image. Finally, the BLMD proposed in this paper is formed. Empirical analysis shows this method can quickly decompose and maintain the characteristics of data-drivenness, adaptability and scale consistency of LMD; it can also avoid the disadvantages of other adaptive processing algorithms, such as the bi-dimensional intrinsic mode function obtained by the decomposition of bi-dimensional empirical mode decomposition and the residual failing to completely contain the feature information of the original image.

Zhi-Wen Liu

Papers

Image Processing Algorithm Based on Bi-dimensional Local Mean Decomposition