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Journal ArticleDOI

Adaptive multifocus image fusion using block compressed sensing with smoothed projected Landweber integration in the wavelet domain.

TL;DR: This paper presents a new approach to multifocus image fusion based on sparse signal representation and it is observed that with fewer samples itself, the proposed method outperforms other approaches.
Abstract: The need for image fusion in current image processing systems is increasing mainly due to the increased number and variety of image acquisition techniques. Image fusion is the process of combining substantial information from several sensors using mathematical techniques in order to create a single composite image that will be more comprehensive and thus more useful for a human operator or other computer vision tasks. This paper presents a new approach to multifocus image fusion based on sparse signal representation. Block-based compressive sensing integrated with a projection-driven compressive sensing (CS) recovery that encourages sparsity in the wavelet domain is used as a method to get the focused image from a set of out-of-focus images. Compression is achieved during the image acquisition process using a block compressive sensing method. An adaptive thresholding technique within the smoothed projected Landweber recovery process reconstructs high-resolution focused images from low-dimensional CS measurements of out-of-focus images. Discrete wavelet transform and dual-tree complex wavelet transform are used as the sparsifying basis for the proposed fusion. The main finding lies in the fact that sparsification enables a better selection of the fusion coefficients and hence better fusion. A Laplacian mixture model fit is done in the wavelet domain and estimation of the probability density function (pdf) parameters by expectation maximization leads us to the proper selection of the coefficients of the fused image. Using the proposed method compared with the fusion scheme without employing the projected Landweber (PL) scheme and the other existing CS-based fusion approaches, it is observed that with fewer samples itself, the proposed method outperforms other approaches.
Citations
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Journal ArticleDOI
Xiaohua Xia1, Yunshi Yao1, Lijuan Yin1, Shida Wu1, Haochen Li1, Zibing Yang1 
TL;DR: A novel pixel-based fusion algorithm is proposed that is superior to the conventional algorithms and the state-of-the-art algorithms in terms of both subjective evaluation and objective evaluation.

14 citations

Journal ArticleDOI
TL;DR: In this study, a multilevel compressive sensing (CS) compression for magnetic resonance imaging (MRI) images is presented and a significant improvement in peak-signal-to-noise ratio and compression level (CL) is shown when compared with a uniform CL algorithm.
Abstract: In this study, a multilevel compressive sensing (CS) compression for magnetic resonance imaging (MRI) images is presented. The proposed algorithm divides the image into frames of equal size, transforms the pixels inside the frame into the sparse domain, and then applies the CS compression to each frame with different level of compression. Four levels of compression are suggested, based on how sparse is the information inside the frame. The proposed algorithm is evaluated using six real MRI images showing different parts of the human body. The experimental results show a significant improvement of 7.03 dB in peak-signal-to-noise ratio and 23.76% in compression level (CL) when compared with a uniform CL algorithm.

11 citations

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors proposed a multiscale feature pyramid network based on activity level weight selection (MFPN-AWS) with a complete downsampling-upsampling structure.
Abstract: At present, deep-learning-based infrared and visible image fusion methods have the problem of extracting insufficient source image features, causing imbalanced infrared and visible information in fused images. To solve the problem, a multiscale feature pyramid network based on activity level weight selection (MFPN-AWS) with a complete downsampling-upsampling structure is proposed. The network consists of three parts: a downsampling convolutional network, an AWS fusion layer, and an upsampling convolutional network. First, multiscale deep features are extracted by downsampling convolutional networks, obtaining rich information of intermediate layers. Second, AWS highlights the advantages of the l1-norm and global pooling dual fusion strategy to describe the characteristics of target saliency and texture detail, and effectively balances the multiscale infrared and visible features. Finally, multiscale fused features are reconstructed by the upsampling convolutional network to obtain fused images. Compared with nine state-of-the-art methods via the publicly available experimental datasets TNO and VIFB, MFPN-AWS reaches more natural and balanced fusion results, such as better overall clarity and salient targets, and achieves optimal values on two metrics: mutual information and visual fidelity.

2 citations

Peer Review
TL;DR: This paper makes a contribution to the performance evaluation of 12 alternative classification strategies on datasets of breast cancer, and the right explanations for the classifiers' dominance were investigated.
Abstract: Received Jun 10, 2022 Revised Aug 13, 2022 Accepted Oct 6, 2022 COVID-19 has caused disruptions to many aspects of everyday life. To reduce the impact of this pandemic, its spreading must be controlled via face mask wearing. Manually mask-checking for everybody is embarrassing and uncontrollable. Hence, the proposed technique is used to help for automatic mask-checking based on deep learning platforms with real-time surveillance live infra-red (IR) camera. In this paper, two recent object detection platforms, named, you only look once version 3 (YOLOv3) and TensorFlow lite are adopted to accomplish this task. The two models are trained with a dataset consisting of images of persons with/without masks. This work is simulated with Google Colab then tested in real-time on an embedded device mated with fast GPU called Raspberry Pi 4 model B, 8 GB RAM. A comparison is made between the two models to verify their performance in relation to their precision rate and processing time. The work of this paper is also succeeded to realize multiple face masks real-time detection up to 10 facemasks in a single scene with high inference speed. Temperature is also measured using IR touchless sensor for each person with sound alarming to alert fever. The presented detector is cheap, light, small, and fast, with 99% accuracy rate during training and testing.
References
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Journal ArticleDOI
TL;DR: The theory of compressive sampling, also known as compressed sensing or CS, is surveyed, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition.
Abstract: Conventional approaches to sampling signals or images follow Shannon's theorem: the sampling rate must be at least twice the maximum frequency present in the signal (Nyquist rate). In the field of data conversion, standard analog-to-digital converter (ADC) technology implements the usual quantized Shannon representation - the signal is uniformly sampled at or above the Nyquist rate. This article surveys the theory of compressive sampling, also known as compressed sensing or CS, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition. CS theory asserts that one can recover certain signals and images from far fewer samples or measurements than traditional methods use.

9,686 citations

Journal ArticleDOI
TL;DR: Several methods for filter design are described for dual-tree CWT that demonstrates with relatively short filters, an effective invertible approximately analytic wavelet transform can indeed be implemented using the dual- tree approach.
Abstract: The paper discusses the theory behind the dual-tree transform, shows how complex wavelets with good properties can be designed, and illustrates a range of applications in signal and image processing The authors use the complex number symbol C in CWT to avoid confusion with the often-used acronym CWT for the (different) continuous wavelet transform The four fundamentals, intertwined shortcomings of wavelet transform and some solutions are also discussed Several methods for filter design are described for dual-tree CWT that demonstrates with relatively short filters, an effective invertible approximately analytic wavelet transform can indeed be implemented using the dual-tree approach

2,407 citations

Journal ArticleDOI
TL;DR: In this article, an image fusion scheme based on the wavelet transform is presented, where wavelet transforms of the input images are appropriately combined, and the new image is obtained by taking the inverse wavelet transformation of the fused wavelet coefficients.

1,532 citations

Journal ArticleDOI
TL;DR: This letter presents a locally adaptive denoising algorithm using the bivariate shrinkage function and is illustrated using both the orthogonal and dual tree complex wavelet transforms.
Abstract: The performance of image-denoising algorithms using wavelet transforms can be improved significantly by taking into account the statistical dependencies among wavelet coefficients as demonstrated by several algorithms presented in the literature. In two earlier papers by the authors, a simple bivariate shrinkage rule is described using a coefficient and its parent. The performance can also be improved using simple models by estimating model parameters in a local neighborhood. This letter presents a locally adaptive denoising algorithm using the bivariate shrinkage function. The algorithm is illustrated using both the orthogonal and dual tree complex wavelet transforms. Some comparisons with the best available results are given in order to illustrate the effectiveness of the proposed algorithm.

617 citations

Journal ArticleDOI
Bin Yang1, Shutao Li1
TL;DR: A sparse representation-based multifocus image fusion method that can simultaneously resolve the image restoration and fusion problem by changing the approximate criterion in the sparse representation algorithm is proposed.
Abstract: To obtain an image with every object in focus, we always need to fuse images taken from the same view point with different focal settings. Multiresolution transforms, such as pyramid decomposition and wavelet, are usually used to solve this problem. In this paper, a sparse representation-based multifocus image fusion method is proposed. In the method, first, the source image is represented with sparse coefficients using an overcomplete dictionary. Second, the coefficients are combined with the choose-max fusion rule. Finally, the fused image is reconstructed from the combined sparse coefficients and the dictionary. Furthermore, the proposed fusion scheme can simultaneously resolve the image restoration and fusion problem by changing the approximate criterion in the sparse representation algorithm. The proposed method is compared with spatial gradient (SG)-, morphological wavelet transform (MWT)-, discrete wavelet transform (DWT)-, stationary wavelet transform (SWT)-, curvelet transform (CVT)-, and nonsubsampling contourlet transform (NSCT)-based methods on several pairs of multifocus images. The experimental results demonstrate that the proposed approach performs better in both subjective and objective qualities.

571 citations