Multi-focus Image Fusion Algorithm Research Articles

Multi-Focus Image Fusion Algorithm Based on Multi-Task Learning and PS-ViT

Multi-Focus Image Fusion Algorithm Based on Multi-Task Learning and PS-ViT

Multi-Focus Image Fusion Using Energy Valley Optimization Algorithm

When a natural scene is photographed using imaging sensors commonly used today, part of the image is obtained sharply while the other part is obtained blurry. This problem is called limited depth of field. This problem can be solved by fusing the sharper parts of multi-focus images of the same scene. These methods are called multi-focus image fusion methods. This study proposes a block-based multi-focus image fusion method using the Energy Valley Optimization Algorithm (EVOA), which has been introduced in recent years. In the proposed method, the source images are first divided into uniform blocks, and then the sharper blocks are determined using the criterion function. By fusing these blocks, a fused image is obtained. EVOA is used to optimize the block size. The function that maximizes the quality of the fused image is used as the fitness function of the EVOA. The proposed method has been applied to commonly used image sets. The obtained experimental results are compared with the well-known Genetic Algorithm (GA), Differential Evolution Algorithm (DE), and Artificial Bee Colony Optimization Algorithm (ABC). The experimental results show that EVOA can compete with the other block-based multi-focus image fusion algorithms.

Multi-focus image fusion algorithm based on region detection and ACS network

Multi-focus image fusion algorithm based on region detection and ACS network

Algorithm for Multifocus Image Fusion Based on Low-Rank and Sparse Matrix Decomposition and Discrete Cosine Transform

Algorithm for Multifocus Image Fusion Based on Low-Rank and Sparse Matrix Decomposition and Discrete Cosine Transform

Multiscale Dilated U-Net Based Multifocus Image Fusion Algorithm

Multiscale Dilated U-Net Based Multifocus Image Fusion Algorithm

Multi-Focus Image Fusion via Distance-Weighted Regional Energy and Structure Tensor in NSCT Domain.

In this paper, a multi-focus image fusion algorithm via the distance-weighted regional energy and structure tensor in non-subsampled contourlet transform domain is introduced. The distance-weighted regional energy-based fusion rule was used to deal with low-frequency components, and the structure tensor-based fusion rule was used to process high-frequency components; fused sub-bands were integrated with the inverse non-subsampled contourlet transform, and a fused multi-focus image was generated. We conducted a series of simulations and experiments on the multi-focus image public dataset Lytro; the experimental results of 20 sets of data show that our algorithm has significant advantages compared to advanced algorithms and that it can produce clearer and more informative multi-focus fusion images.

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

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.

A Convolutional Neural Network-Based Conditional Random Field Model for Structured Multi-Focus Image Fusion Robust to Noise

The limited depth of field of optical lenses, makes multi-focus image fusion (MFIF) algorithms of vital importance. Lately, Convolutional Neural Networks (CNN) have been widely adopted in MFIF methods, however their predictions mostly lack structure and are limited by the size of the receptive field. Moreover, since images have noise due to various sources, the development of MFIF methods robust to image noise is required. A novel robust to noise Convolutional Neural Network-based Conditional Random Field (mf-CNNCRF) model is introduced. The model takes advantage of the powerful mapping between input and output of CNN networks and the long range interactions of the CRF models in order to reach structured inference. Rich priors for both unary and smoothness terms are learned by training CNN networks. The α -expansion graph-cut algorithm is used to reach structured inference for MFIF. A new dataset, which includes clean and noisy image pairs, is introduced and is used to train the networks of both CRF terms. A low-light MFIF dataset is also developed to demonstrate real-life noise introduced by the camera sensor. Qualitative and quantitative evaluation prove that mf-CNNCRF outperforms state-of-the-art MFIF methods for clean and noisy input images, while being more robust to different noise types without requiring prior knowledge of noise.

Multi-focus image fusion algorithm based on SML and difference image

Multi-focus image fusion algorithm based on SML and difference image

Multi-Scale Dilated Convolutional Neural Network Based Multi-Focus Image Fusion Algorithm

Multi-Scale Dilated Convolutional Neural Network Based Multi-Focus Image Fusion Algorithm

An Enhancement Process for Multi-focus Images Resulted from Image Fusion using qshiftN DTCWT and MPCA in Multiresolution Domain

Images having different focus values are combined using image fusion methodologies. The fused image shows superiority in the quality and highly informative than compared o any other multifocus images. The fused image would show its suitability for visual perception object detection due to its high quality than compared to multifocus images. Among the various quality parameters of the fused image, directional invariance and shift invariance significantly influence the fused image quality. The traditional image fusion methods using wavelets severely suffers with poor invariance and lack of directionality. An efficient image fusion method is developed using DTCWT with qshiftN and MPCA algorithms in the multiresolution (MR) domain. Multifocus input images are decomposed into high and low frequency components using MR algorithm. The decomposed frequency components of the input images are fused using DTCWT with qshiftN algorithm. The proposed fusion algorithm preserves both sift invariance and directional properties of the multifocus source image. Lastly, the fused image is processed using MPCA algorithm to enhance the features. The proposed methodology is assessed using various multifocus images and the evaluated metrics are contrasted with technologically advanced algorithms reported recently. The statistical metrics evaluated for the proposed method for different multifocus images shows superior properties than compared to the recently reported multifocus image fusion algorithms.

Multi-Focus Image Fusion Based on Hessian Matrix Decomposition and Salient Difference Focus Detection.

Multi-focus image fusion integrates images from multiple focus regions of the same scene in focus to produce a fully focused image. However, the accurate retention of the focused pixels to the fusion result remains a major challenge. This study proposes a multi-focus image fusion algorithm based on Hessian matrix decomposition and salient difference focus detection, which can effectively retain the sharp pixels in the focus region of a source image. First, the source image was decomposed using a Hessian matrix to obtain the feature map containing the structural information. A focus difference analysis scheme based on the improved sum of a modified Laplacian was designed to effectively determine the focusing information at the corresponding positions of the structural feature map and source image. In the process of the decision-map optimization, considering the variability of image size, an adaptive multiscale consistency verification algorithm was designed, which helped the final fused image to effectively retain the focusing information of the source image. Experimental results showed that our method performed better than some state-of-the-art methods in both subjective and quantitative evaluation.

Multi-focus image fusion dataset and algorithm test in real environment.

DATA REPORT article Front. Neurorobot., 18 October 2022 Volume 16 - 2022 | https://doi.org/10.3389/fnbot.2022.1024742

(Retracted) Application of image fusion algorithm based on digital media technology in modeling design

The product modeling design is the concrete manifestation of realizing the unified identification goal of the corporate image. It can shape and disseminate the corporate image, create individual brands, and win in the fierce market competition. The product modeling design needs to serve the overall image design of the enterprise, with product design as the core, around people’s needs for products, and to the greatest extent possible to meet the needs of individuals and society to obtain a general sense of recognition. It changes people’s way of life and improves the quality and level of life. Different pieces of information need to be combined when designing a product. To achieve the best effect, we aim to study the application of image fusion algorithm based on digital media technology in modeling design. Digital media technology is a technology that combines abstract numbers, physical media, and computer technology. It is a generation of digital media technology for digital audio, digital video, digital film, computer animation, virtual reality, and so on. A compressed sensing multifocus image fusion algorithm based on non-subsampled shearlet transform is proposed. It improves the efficiency of the fusion algorithm without affecting the visual effect. We analyze the shape design of wearable smart products, summarizes the characteristics of product shape design, as well as the aesthetic value is shown, and summarizes the entry point of product shape design. The experimental results of this paper show that the proportion of choosing the classic style is 55.3%, which shows that people are more comfortable with the classic style, so they are more likely to buy. Health monitoring is the mainstream function of wearable products, and its proportion is as high as 71.3%, indicating that people are more concerned about health issues.

Multi-focus image fusion algorithm based on random features embedding and ensemble learning.

Multi-focus image fusion algorithm integrates complementary information from multiple source images to obtain an all-in-focus image. Most published methods will create incorrect points in their decision map which have to be refined and polished with post-processing procedure. Aim to address these problems, we present, for the first time, a novel algorithm based on random features embedding (RFE) and ensemble learning which reduced the calculation workload and improved the accuracy without post-processing. We utilize RFE to approximate a kernel function so that Support Vector Machine (SVM) can be applied to large scale data set. With ensemble learning scheme we then eliminate the abnormal points in the decision map. We reduce the risk of entrap into over-fitting predicament and boost the generalization ability by combining RFE and ensemble learning. The theoretical analysis is in consistence with the experimental results. With low computation cost, the proposed algorithm achieve high visual quality as the state-of-the-art(SOTA).

Convolutional analysis operator learning for multifocus image fusion

Sparse representation (SR), convolutional sparse representation (CSR) and convolutional dictionary learning (CDL) are synthetic-based priors that have proven to be successful in signal inverse problems (such as multifocus image fusion). Unlike “synthesis” formulas, “analysis” model assigns probabilities to signals through various forward measurements of signals. Analysis operator learning (AOL) is a classical analysis-based learning method. Convolutional analysis operator learning (CAOL) is convolutional form of AOL. CAOL uses unsupervised learning method to train autocoded convolutional neural network (CNN) to more accurately solve inverse problem. From the perspective of CAOL, this paper introduces learned convolutional regularizers into multifocus image fusion and proposes CAOL-based multifocus image fusion algorithm. In the CDL stage, convergent block proximal extrapolated gradient method with majorizer (BPEG-M) and adaptive momentum restarting scheme are used. In the sparse fusion stage, alternating direction method of multipliers (ADMM) approach with convolutional basis pursuit denoising (CBPDN) and l1 norm maximum strategy are employed for high-frequency and low-frequency component, respectively. 3 types of multifocus images (static gray images, gray images in sports and color images) are tested to verify performance of the proposed method. A comparison with representative methods demonstrates superiority of our method in terms of subjective observation and objective evaluation.

HoEnTOA: Holoentropy and Taylor Assisted Optimization based Novel Image Quality Enhancement Algorithm for Multi-Focus Image Fusion

HoEnTOA: Holoentropy and Taylor Assisted Optimization based Novel Image Quality Enhancement Algorithm for Multi-Focus Image Fusion

Multi-focus Image Fusion Based on Random Walk

An effective multi-focus image fusion algorithm based on random walk is proposed in this paper. Random walk and guided filter have attracted extensive attention in image fusion. Random walk is usually used to solve probability problems and it has a good smoothing effect, and guided filter can preserve the gradient information of the image well. The combination of two algorithms can better retain the edge information of the input image. Six sets of source images and five existing methods are used in the experiment and the experimental results show that the proposed algorithm outperforms the existing methods in both subjective and objective evaluation.

Optimal fusion of multi-focus image: Integrating WNMF and focal point analysis

Image fusion can be used to improve the image utilization, spatial resolution and spectral resolution, which has been widely applied on medicine, remote sensing, computer vision, weather forecast and military target recognition. The goal of image fusion is to reduce the uncertainty and redundancy of the output and increase the reliability of the image on the basis of the maximum combination of relevant information. In this paper, a multi-focus image fusion algorithm based on WNMF and Focal point position analysis is proposed to improve the image fusion method based on nonnegative matrix factorization. In the imaging process, the Gaussian function is used to approximate the point spread function in the optical system. Then calculate the difference between the original image and the approximate point spread function and get the weighted matrix [Formula: see text]. Finally, we apply the weighted nonnegative matrix algorithm to image fusion, and the new fusion image with clear parts is obtained. Experimental results show that the multi-focus image fusion algorithm based on WNMF and Focal point position analysis (MFWF) is better.

Exploiting Superpixels for Multi-Focus Image Fusion.

Multi-focus image fusion is the process of combining focused regions of two or more images to obtain a single all-in-focus image. It is an important research area because a fused image is of high quality and contains more details than the source images. This makes it useful for numerous applications in image enhancement, remote sensing, object recognition, medical imaging, etc. This paper presents a novel multi-focus image fusion algorithm that proposes to group the local connected pixels with similar colors and patterns, usually referred to as superpixels, and use them to separate the focused and de-focused regions of an image. We note that these superpixels are more expressive than individual pixels, and they carry more distinctive statistical properties when compared with other superpixels. The statistical properties of superpixels are analyzed to categorize the pixels as focused or de-focused and to estimate a focus map. A spatial consistency constraint is ensured on the initial focus map to obtain a refined map, which is used in the fusion rule to obtain a single all-in-focus image. Qualitative and quantitative evaluations are performed to assess the performance of the proposed method on a benchmark multi-focus image fusion dataset. The results show that our method produces better quality fused images than existing image fusion techniques.