Multiscale Image Matting Based Multi-Focus Image Fusion Technique

Maqsood, Sarmad; Javed, Umer; Riaz, Muhammad Mohsin; Muzammil, Muhammad; Muhammad, Fazal; Kim, Sung Hwan

doi:10.3390/electronics9030472

Cited by 18 publications

(12 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, the MST and Sparse Representation (SR) techniques have gained significant popularity in the transform domain and have produced positive results in medical image analysis [26]. However, these methods have shortcomings, such as (i) the "max-l1" rule induces spatial inconsistency in a fused image when different modalities are captured from the source images [27], (ii) the MST-based filters used for the SR-based image fusion [28] are time-dependent due to the training of dictionary and its optimization, and (iii) these algorithms are also unable to decompose several types of images [12]. Another challenge is the complicated oriented shape of source images that cannot be precisely categorized through an already trained dictionary [28].…”

Section: Related Workmentioning

confidence: 99%

“…A CT image gives information about hard tissues and their structures, whereas an MRI image indicates information regarding soft tissues. For better diagnosis, it is essential to merge critical information of the aforementioned images into one fused image [12]. In this regard, the aforementioned set of algorithms perform multimodal image fusion.…”

Section: Qualitative Analysis Of the Given Set Of Algorithms For Multmentioning

confidence: 99%

“…These images provide anatomical statistics [7]; however, the extraction of purposeful functional details from an individual image remains a critical issue. This demands multimodal image fusion, which integrates the complementary information of images from different modalities to produce an enhanced fused image through simulation, thereby providing enriched anatomical and functional information [6,7,[11][12][13].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

CSID: A Novel Multimodal Image Fusion Algorithm for Enhanced Clinical Diagnosis

et al. 2020

Self Cite

View full text Add to dashboard Cite

Technology-assisted clinical diagnosis has gained tremendous importance in modern day healthcare systems. To this end, multimodal medical image fusion has gained great attention from the research community. There are several fusion algorithms that merge Computed Tomography (CT) and Magnetic Resonance Images (MRI) to extract detailed information, which is used to enhance clinical diagnosis. However, these algorithms exhibit several limitations, such as blurred edges during decomposition, excessive information loss that gives rise to false structural artifacts, and high spatial distortion due to inadequate contrast. To resolve these issues, this paper proposes a novel algorithm, namely Convolutional Sparse Image Decomposition (CSID), that fuses CT and MR images. CSID uses contrast stretching and the spatial gradient method to identify edges in source images and employs cartoon-texture decomposition, which creates an overcomplete dictionary. Moreover, this work proposes a modified convolutional sparse coding method and employs improved decision maps and the fusion rule to obtain the final fused image. Simulation results using six datasets of multimodal images demonstrate that CSID achieves superior performance, in terms of visual quality and enriched information extraction, in comparison with eminent image fusion algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Qualitative Analysis Of the Given Set Of Algorithms For Multmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

CSID: A Novel Multimodal Image Fusion Algorithm for Enhanced Clinical Diagnosis

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The presmoothing operation (9) mitigates the effect of noise on each gradient map, and the aggregation (10) makes sure the information from gradients are maximally utilized in the edge detection procedure. Such a fusion multiscale gradient maps is useful in capturing spatial gradient changes and can aid in improving the information content overall [28] We thus utilize the summed up gradient maps along with gray scale values and apply the FI measure based thresholded value selection for this combined image,…”

Section: B Multiscale Gradient Fusion With Fisher Informationmentioning

confidence: 99%

Multiscale Gradient Maps Augmented Fisher Information-Based Image Edge Detection

et al. 2020

View full text Add to dashboard Cite

Image edge detection is an important task in image processing and pattern recognition. Edges in digital images signify image discontinuities and traditionally gradient information is utilized in finding possible edge pixels. In this work, we consider a fusion approach using multiscale gradient maps along with non-parametric Fisher information which is recently used in edge detection. By using multiscale gradient maps we obtain better edge localization and robust edge maps and local thresholding with Fisher information helps obtain better detection. Experimental results on a variety of digital images and performance evaluation undertaken in comparison with edge detectors from the literature show the advantage of the proposed approach.

show abstract

“…During face alignment, only a single face is considered, and the prior information from the set of face images is used to train the model and constrain the 1 alignment process. Joint alignment [10] is usually used as the optimization way to the alignment results of image faces. It considers 1 This work was supported by the National Natural Science Foundation of China under Grant 61372176. multiple faces at the same time, and optimizes these results by using the constraints among these faces.…”

Section: Introductionmentioning

confidence: 99%

Joint Alignment of Image Faces

et al. 2020

View full text Add to dashboard Cite

Researches on face alignment have made great progress, which benefits from the use of prior information and auxiliary models. However, that information lacks in a single face image has always affected the further development of these researches. The methods considering multiple face images provide a feasible way to solve the problem undoubtedly. Joint alignment where multiple face images are considered was presented in the paper. Face alignment was used for each face, and joint face alignment was used for optimizing the alignment results of all faces further. During joint alignment, both rigid variations of faces and non-rigid distortions were considered, however, they were regarded as two independent stages. Joint face alignment was a process where optimization was performed iteratively. In each iteration, both rigid variations and non-rigid distortions were performed sequentially, and moreover, the results of rigid variations were used as input of non-rigid distortions. At the stage of rigid variations, the key points of a face were divided into five groups to reduce the effect of global constraints which was imposed by face shape. After several iterations, the optimal solution of joint alignment can be obtained. The experimental results show that the joint alignment can obtain the optimal results than joint alignment using phased global rigid variations and non-rigid distortions and that using iterative global rigid variations and non-rigid distortions, and it can be used as a novel method for joint alignment.

show abstract

Multiscale Image Matting Based Multi-Focus Image Fusion Technique

Cited by 18 publications

References 33 publications

CSID: A Novel Multimodal Image Fusion Algorithm for Enhanced Clinical Diagnosis

CSID: A Novel Multimodal Image Fusion Algorithm for Enhanced Clinical Diagnosis

Multiscale Gradient Maps Augmented Fisher Information-Based Image Edge Detection

Joint Alignment of Image Faces

Contact Info

Product

Resources

About