Mathematical modeling and simulation of multi-focus image fusion techniques using the effect of image enhancement criteria: a systematic review and performance evaluation

Choudhary, Gaurav; Sethi, Dinesh

doi:10.1007/s10462-023-10487-3

Cited by 7 publications

(5 citation statements)

References 187 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, fusion based on generative adversarial network (GAN) [31,32] and transformer [33] are some notable works published in these years. More specifically, these methods can be classified into regression and classification models [2]. Despite the success of these methods, training requires a huge amount of data and is an essential step for fusion.…”

Section: Related Methodsmentioning

confidence: 99%

“…This blended image incorporates data from both the CT scan and MRI scans, hence enhancing the diagnostic capabilities. Moreover, this idea has been successfully implemented in other domains, including but not limited to industry, medicine, biology, and military navigation [2]. The field of image processing has experienced substantial progress in both theoretical frameworks and algorithmic approaches, leading to notable developments in the domain of image fusion.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, many methodologies exhibit limitations in their applicability to specific datasets [5]. To mitigate this limitation, alternative adaptive decomposition methods such as empirical mode decomposition (EMD) [6], Hilbert vibration decomposition (HVD) [7], and some multiple transform-based methods known as combined techniques [2] have also been proposed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

HVDFusion: An effective fusion framework based on Hilbert vibration decomposition for multi-focal and multi-sensor images

Choudhary,

Sethi

2024

Preprint

Self Cite

View full text Add to dashboard Cite

Image fusion (IF) due to its cost-effectiveness and applicability in a broader range of applications makes it an emerging area in research. However, it is seen from the literature that most of the existing fusion algorithms are application-specific. As a result, the results obtained for different applications are limited. So, in this work, we propose an effective algorithm for better outcomes for different applications. For this, an adaptive image decomposition tool known as Hilbert vibration decomposition (HVD) is used. HVD decomposes an image into instantaneous energy components having amplitudes (image amplitudes) and frequencies. Unlike traditional multi-scale decomposition, the adaptive decomposition strategy used by HVD does not require any fixed cut-off frequency or pre-defined function basis and offers better spatial resolution. Then, we compute instantaneous detailed image amplitudes that generally contain significant information. These are obtained by subtracting the instantaneous image amplitudes from the source images. Further, we find the optimized weights with the help of a statistical approach, i.e., by using unbiased estimates and eigenvalues related to these instantaneous detailed image amplitudes. After this computation, the optimized weights are integrated with source images to generate the final fused image. The simulation of the proposed work is carried out in MATLAB software for multi-focus, medical, and visible-infrared (VI-IR) image samples and compared with existing methods. It is seen that in comparison to traditional as well as some deep learning-based fusion works, the proposed work not only provides better/comparative outputs qualitatively and quantitatively but there is also less computational time complexity.

show abstract

Section: Related Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

HVDFusion: An effective fusion framework based on Hilbert vibration decomposition for multi-focal and multi-sensor images

Choudhary,

Sethi

2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…To enhance the quality of medical images, numerous image enhancement techniques have been proposed. One popular approach is image fusion, which is described as the process of improving an image's resolution by combining numerous copies of the image with previously recorded data that are notably distinct from one another [10]. In the domains of image processing and computer vision, multi-exposure image fusion is becoming a prominent area of study because it can merge images with different exposure levels into a high-quality full exposure image [11].…”

Section: Introductionmentioning

confidence: 99%

Endoscopic Image Enhancement: Wavelet Transform and Guided Filter Decomposition-Based Fusion Approach

Moghtaderi,

Yaghoobian,

Wahid

et al. 2024

J. Imaging

View full text Add to dashboard Cite

Endoscopies are helpful for examining internal organs, including the gastrointestinal tract. The endoscope device consists of a flexible tube to which a camera and light source are attached. The diagnostic process heavily depends on the quality of the endoscopic images. That is why the visual quality of endoscopic images has a significant effect on patient care, medical decision-making, and the efficiency of endoscopic treatments. In this study, we propose an endoscopic image enhancement technique based on image fusion. Our method aims to improve the visual quality of endoscopic images by first generating multiple sub images from the single input image which are complementary to one another in terms of local and global contrast. Then, each sub layer is subjected to a novel wavelet transform and guided filter-based decomposition technique. To generate the final improved image, appropriate fusion rules are utilized at the end. A set of upper gastrointestinal tract endoscopic images were put to the test in studies to confirm the efficacy of our strategy. Both qualitative and quantitative analyses show that the proposed framework performs better than some of the state-of-the-art algorithms.

show abstract

“…Improved medical images enable more accurate diagnoses, potentially leading to early diagnosis and treatment of diseases. Additionally, fusion methods can reduce radiation exposure of patients during medical image operations [8]. The article is organized into different sections namely (2) related works, (3) methods used in the proposed work, (4) results and discussion of all the comparisons centered on metrics and output screenshots of the proposed application, and (5) conclusion of the proposed work and further studies.…”

Section: Introductionmentioning

confidence: 99%

Medical Image Fusion for Brain Tumor Diagnosis Using Effective Discrete Wavelet Transform Methods

Ramaraj,

Venkatachalaappaswamy,

Sankar

2024

J. Inf. Syst. Eng. Bus. Intell.

View full text Add to dashboard Cite

Background: The field of clinical or medical imaging is beginning to experience significant advancements in recent years. Various medical imaging methods such as computed tomography (CT), X-radiation (X-ray), and magnetic resonance imaging (MRI) produce images with distinct resolution differences, goals, and noise levels, making it challenging for medical experts to diagnose diseases. Objective: The limitations of a single medical image modality have increased the necessity for medical image fusion. The proposed solution is to create a fusion method of merging two types of medical images, such as MRI and CT. Therefore, this study aimed to develop a software solution that swiftly identifies the precise region of a brain tumor, speeding up the diagnosis and treatment planning. Methods: The proposed methodology combined clinical images by using discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT). This strategy depended on a multi-goal decay of the image information using DWT, and high-frequency sub-bands of the disintegrated images were combined using a weighted averaging method. Meanwhile, the low-frequency sub-bands were straight-forwardly replicated in the resulting image. The combined high-quality image was recreated using the IDWT. This method can handle images with various modalities and resolutions without the need for previous data. Results: The results showed that the outcomes of the proposed method were assessed by different metrics such as accuracy, recall, F1-score, and visual quality. The method showed a high accuracy of 98% over the familiar neural network techniques. Conclusion: The proposed method was found to be computationally effective and produced high-quality medical images to assist professionals. Furthermore, the method can be stretched out to other image modalities and exercised by hybrid techniques of wavelet transform and neural networks and used for different clinical image analysis tasks. Keywords: CT and MRI, Image fusion, brain tumor, wavelet transform methods, medical images, machine learning, CNN

show abstract

Mathematical modeling and simulation of multi-focus image fusion techniques using the effect of image enhancement criteria: a systematic review and performance evaluation

Cited by 7 publications

References 187 publications

HVDFusion: An effective fusion framework based on Hilbert vibration decomposition for multi-focal and multi-sensor images

HVDFusion: An effective fusion framework based on Hilbert vibration decomposition for multi-focal and multi-sensor images

Endoscopic Image Enhancement: Wavelet Transform and Guided Filter Decomposition-Based Fusion Approach

Medical Image Fusion for Brain Tumor Diagnosis Using Effective Discrete Wavelet Transform Methods

Contact Info

Product

Resources

About