Improvement in CNN-Based Multifocus Image Fusion Algorithm with Triangulated Fuzzy Filter

2021 ◽  
pp. 2150020
Author(s):  
Meenu Manchanda ◽  
Deepak Gambhir
Keyword(s):  
Image Fusion ◽  
Situational Awareness ◽  
Similarity Index ◽  
Fusion Rule ◽  
Structural Similarity ◽  
Research Field ◽  
Fuzzy Filter ◽  
Fusion Algorithm ◽  
Strength Formula ◽  
Index Measure

Multifocus image fusion is a demanding research field due to the utilization of modern imaging devices. Generally, the scene to be captured contains objects at different distances from these devices and so a set of multifocus images of the scene is captured with different objects in-focus. However, to improve the situational awareness of the captured scene, these sets of images are required to be fused together. Therefore, a multifocus image fusion algorithm based on Convolutional Neural Network (CNN) and triangulated fuzzy filter is proposed. A CNN is used to extract information regarding focused pixels of input images and the same is used as fusion rule for fusing the input images. The focused information so extracted may still need to be refined near the boundaries. Therefore, asymmetrical triangular fuzzy filter with the median center (ATMED) is employed to correctly classify the pixels near the boundary. The advantage of using this filter is to rely on precise detection results since any misdetection may considerably degrade the fusion quality. The performance of the proposed algorithm is compared with the state-of-art image fusion algorithms, both subjectively and objectively. Various parameters such as edge strength ([Formula: see text]), fusion loss (FL), fusion artifacts (FA), entropy ([Formula: see text]), standard deviation (SD), spatial frequency (SF), structural similarity index measure (SSIM) and feature similarity index measure (FSIM) are used to evaluate the performance of the proposed algorithm. Experimental results proved that the proposed fusion algorithm produces a fused image that contains all-in-one focused pixels and is better than those obtained using other popular and latest image fusion works.

Medical Image Fusion in Wavelet and Ridgelet Domains

2017 ◽  
pp. 711-723
Author(s):  
Vikrant Bhateja ◽  
Abhinav Krishn ◽  
Himanshi Patel ◽  
Akanksha Sahu
Keyword(s):  
Image Fusion ◽  
Information Content ◽  
Medical Image ◽  
Similarity Index ◽  
Principal Component ◽  
Structural Similarity ◽  
Fusion Algorithm ◽  
Medical Image Fusion ◽  
Multi Scale ◽  
Fused Image

Medical image fusion facilitates the retrieval of complementary information from medical images and has been employed diversely for computer-aided diagnosis of life threatening diseases. Fusion has been performed using various approaches such as Pyramidal, Multi-resolution, multi-scale etc. Each and every approach of fusion depicts only a particular feature (i.e. the information content or the structural properties of an image). Therefore, this paper presents a comparative analysis and evaluation of multi-modal medical image fusion methodologies employing wavelet as a multi-resolution approach and ridgelet as a multi-scale approach. The current work tends to highlight upon the utility of these approaches according to the requirement of features in the fused image. Principal Component Analysis (PCA) based fusion algorithm has been employed in both ridgelet and wavelet domains for purpose of minimisation of redundancies. Simulations have been performed for different sets of MR and CT-scan images taken from ‘The Whole Brain Atlas'. The performance evaluation has been carried out using different parameters of image quality evaluation like: Entropy (E), Fusion Factor (FF), Structural Similarity Index (SSIM) and Edge Strength (QFAB). The outcome of this analysis highlights the trade-off between the retrieval of information content and the morphological details in finally fused image in wavelet and ridgelet domains.

scholarly journals Edge Preserving Fuzzy Filter for Suppression of Impulse Noise in Images

2019 ◽  
Vol 8 (6) ◽  
pp. 1627-1633
Keyword(s):  
Impulse Noise ◽  
Similarity Index ◽  
Structural Similarity ◽  
Fuzzy Filter ◽  
Structural Similarity Index ◽  
Edge Preserving ◽  
Edge Pixel ◽  
Noisy Pixel ◽  
Simulation Results ◽  
Index Measure

Impulse noise in images culminates to the loss of valuable information. This paper proposes an efficient fuzzy filter for suppression of impulse noise in images (EFFSIN). Firstly, impulse noise is effectively detected by the proposed algorithm with the utilization of Fuzzy C-Means (FCM) in two stages.Only legitimate pixels are restored after fair distiction of noisy pixel from edge pixel. Proper restoration of noisy pixel following effective detection by proposed work leads to low miss detection(MD) and false alarm (FA) rates. Simulation results depict the efficacy of the proposed filter with reference to peaksignal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).

Fused Image Quality Measure Based on Structural Similarity

2011 ◽  
Vol 255-260 ◽  
pp. 2072-2076
Author(s):  
Yi Yong Han ◽  
Jun Ju Zhang ◽  
Ben Kang Chang ◽  
Yi Hui Yuan ◽  
Hui Xu
Keyword(s):  
Image Fusion ◽  
Structural Information ◽  
Similarity Index ◽  
Structural Similarity ◽  
Reference Image ◽  
Human Visual Perception ◽  
Subjective Evaluations ◽  
Fused Image ◽  
Fusion Methods ◽  
Image Quality Measure

Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we present a new approach using structural similarity index for assessing quality in image fusion. The advantages of our measures are that they do not require a reference image and can be easily computed. Numerous simulations demonstrate that our measures are conform to subjective evaluations and can be able to assess different image fusion methods.

scholarly journals Multimodal Medical Image Fusion using Guided Filter in NSCT Domain

2018 ◽  
Vol 11 (4) ◽  
pp. 1937-1946
Author(s):  
Nancy Mehta ◽  
Sumit Budhiraja
Keyword(s):  
Image Fusion ◽  
Medical Image ◽  
Similarity Index ◽  
Low Frequency ◽  
Structural Similarity ◽  
Relevant Information ◽  
Phase Congruency ◽  
Edge Preserving ◽  
Medical Image Fusion ◽  
Fused Image

Multimodal medical image fusion aims at minimizing the redundancy and collecting the relevant information using the input images acquired from different medical sensors. The main goal is to produce a single fused image having more information and has higher efficiency for medical applications. In this paper modified fusion method has been proposed in which NSCT decomposition is used to decompose the wavelet coefficients obtained after wavelet decomposition. NSCT being multidirectional,shift invariant transform provide better results.Guided filter has been used for the fusion of high frequency coefficients on account of its edge preserving property. Phase congruency is used for the fusion of low frequency coefficients due to its insensitivity to illumination contrast hence making it suitable for medical images. The simulated results show that the proposed technique shows better performance in terms of entropy, structural similarity index, Piella metric. The fusion response of the proposed technique is also compared with other fusion approaches; proving the effectiveness of the obtained fusion results.

Denoising and 3D Reconstruction of CT Images in Extracted Tooth via Wavelet and Bilateral Filtering

2018 ◽  
Vol 32 (05) ◽  
pp. 1854010 ◽  
Author(s):  
Cuizhen Wang ◽  
Zhenxue Chen ◽  
Yan Wang ◽  
Zhifeng Wang
Keyword(s):  
3D Reconstruction ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Similarity Index ◽  
Low Frequency ◽  
Structural Similarity ◽  
Bilateral Filtering ◽  
Denoising Method ◽  
Dimensional Reconstruction ◽  
Index Measure

Three-dimensional reconstruction of teeth plays an important role in the operation of living dental implants. However, the tissue around teeth and the noise generated in the process of image acquisition bring a serious impact on the reconstruction results, which must be reduced or eliminated. Combined with the advantages of wavelet transform and bilateral filtering, this paper proposes an image denoising method based on the above methods. The method proposed in this paper not only removes the noise but also preserves the image edge details. The noise in high frequency subbands is denoised using a locally adaptive thresholding and the noise in low frequency subbands is filtered by the bilateral filtering. Peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and 3D reconstruction using the iso-surface extraction method are used to evaluate the denoising effect. The experimental results show that the proposed method is better than the wavelet denoising and bilateral filtering, and the reconstruction results meet the requirements of clinical diagnosis.

Image Fusion Algorithm Based on Nonsubsampled Contourlet Transform

2013 ◽  
Vol 401-403 ◽  
pp. 1381-1384 ◽  
Author(s):  
Zi Juan Luo ◽  
Shuai Ding
Keyword(s):  
Image Fusion ◽  
Multiple Scales ◽  
Weighted Average ◽  
Fusion Rule ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Fusion Algorithm ◽  
Fused Image ◽  
Focus Image ◽  
Limited Depth

t is mostly difficult to get an image that contains all relevant objects in focus, because of the limited depth-of-focus of optical lenses. The multifocus image fusion method can solve the problem effectively. Nonsubsampled Contourlet transform has varying directions and multiple scales. When the Nonsubsampled contourlet transform is introduced to image fusion, the characteristics of original images are taken better and more information for fusion is obtained. A new method of multi-focus image fusion based on Nonsubsampled contourlet transform (NSCT) with the fusion rule of region statistics is proposed in this paper. Firstly, different focus images are decomposed using Nonsubsampled contourlet transform. Then low-bands are integrated using the weighted average, high-bands are integrated using region statistics rule. Next the fused image will be obtained by inverse Nonsubsampled contourlet transform. Finally the experimental results are showed and compared with those of method based on Contourlet transform. Experiments show that the approach can achieve better results than the method based on contourlet transform.

scholarly journals Tinjauan Literatur Sistematik tentang Structural Similarity Index Measure untuk Deteksi Anomali Gambar

2021 ◽  
Vol 7 (2) ◽  
pp. 75
Author(s):  
Halim Bayuaji Sumarna ◽  
Ema Utami ◽  
Anggit Dwi Hartanto
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Human Visual System ◽  
Vision System ◽  
Similarity Index ◽  
Structural Similarity ◽  
Human Vision ◽  
Human Vision System ◽  
Structural Similarity Index ◽  
Index Measure

Image enhancement merupakan prosedur yang digunakan untuk memproses gambar sehingga dapat memperbaiki atau meningkatkan kualitas gambar agar selanjutnya dapat dianalis untuk tujuan tertentu. Ada banyak algoritma image enhancement yang dapat diterapkan pada suatu gambar, salah satunya dapat menggunakan algoritma structural similarity index measure (SSIM), algoritma ini berfungsi sebagai alat ukur dalam menilai kualitas gambar, bekerja dengan membandingkan fitur structural dari gambar, dan kualitas gambar dijelaskan oleh kesamaan structural. Selain untuk menilai kualitas suatu gambar, SSIM dapat menjadi metode dalam menganalisis perbedaan gambar, sehingga diketahui anomali dari perbandingan dua gambar berdasarkan data structural dari sebuah gambar. Tinjauan literature sistematis ini digunakan untuk menganalisis dan fokus pada algoritma SSIM dalam mengetahui anomaly 2 gambar yang terlihat mirip secara human visual system. Hasil sistematis review menunjukkan bahwa penggunaan algoritma SSIM dalam menilai kualitas gambar berkorelasi kuat dengan HVS (Human Vision System) dan dalam deteksi anomaly gambar menghasilkan akurasi yang berbeda, karena terpengaruh intensitas cahaya dan posisi kamera dalam mengambil gambar sebagai dataset.Kata Kunci— SSIM, anomaly, gambar, deteksiImage enhancement is a procedure used to process images so that they can correct or improve image quality so that they can then be analyzed for specific purposes. Many image enhancement algorithms can be applied to an image. one of the usable methods is the structural similarity index measure (SSIM) algorithm, this algorithm serves as a measuring tool in assessing image quality. It works by comparing the structural features of images, and the image quality is explained by structural similarity. In addition to assessing the quality of an image, SSIM can be a method of analyzing image differences. So, the anomalies are known from the comparison of two images based on the structural data from an image. This systematic literature review is used to analyze and focus on the SSIM algorithm in knowing anomaly 2 images that look similar to the human visual system. Systematic review results show that the use of the SSIM algorithm in assessing image quality is strongly correlated with HVS (Human Vision System). In anomaly detection of images produces different accuracy because it is affected by light intensity and camera position in taking pictures as a dataset.Keywords— SSIM, anomaly, gambar, deteksi

scholarly journals Implementation of Hybrid Algorithm Based on NSCT for Medical Image Fusion

2019 ◽  
Vol 9 (2S3) ◽  
pp. 86-92
Keyword(s):  
Image Fusion ◽  
Hybrid Algorithm ◽  
Similarity Index ◽  
Structural Similarity ◽  
Single Image ◽  
Medical Field ◽  
Structural Similarity Index ◽  
Positron Emission ◽  
Tumor Recognition ◽  
Multimodal Image Fusion

The process of combining the two different modal images into one single image is multimodal image fusion. The resulting image is helpful in the medical field for effective and better detection of disease and the processing of images; surgery, tumor recognition, illnesses, etc. In the only modes of medical images, the merged image attributes cannot be achieved and can be overcome with the image fusion of various modal images. A new hybrid algorithm for directive multimodal image fusion will be built for this paper based on the non-sub-sampled contourlet transformation. The images will be fuse through the use of the proposed techniques and comparison with existing technological techniques, using quantitative and qualitative measures. MRI and positron-emission tomography (PET) are used. Quantitative steps, like the Entropy (EN) and Structural Similarity Index (SSIM), will be taken to verify the algorithms

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