Multi-focus image fusion and super-resolution with convolutional neural network

Yang, Bin; Zhong, Jinying; Li, Yuehua; Chen, Zhongze

doi:10.1142/s0219691317500370

Cited by 38 publications

(16 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is concluded from the results shown in these tables that the proposed algorithm provides better objective results as compared to other algorithms. The statistical results obtained for different multifocus images has shown that the proposed algorithm in this paper shows better performance than compared to other methods published recently elsewhere [24,27,31,36,37,38].…”

Section: Modified Principal Component Analysis (Mpca)mentioning

confidence: 58%

Image Enhancement based on Fusion using 2D LPDCT and Modified PCA

2019

IJEAT

View full text Add to dashboard Cite

The images play a vital role in various fields of applications; medical field is the one, where images more widely used in diagnosis. Best image data analysis results if the quality of the image is high. To attain best image quality some popular techniques are available, among that image fusion is one of the technique, it enhances the information of the image by selecting and merging the significant information from two or more similar multi-focus images. Using the features of image fusion a new technique is proposed in this paper. In proposed technique, fusion of sources images with 2D Laplacian Pyramid Discrete Cosine Transformation (2D LP - DCT) and Modified Principal Component Analysis (MPCA). In this, two similar multi-focus images are considered, first, they undergone to 2D LP-DCT and then MPCA technique. The 2D LP-DCT enhances important image features, which are best utilized in image fusion and results good image quality. In Modified PCA, the concept of dimensionality reduction is used. The experimental results indicate that the suggested strategy can produce fused images with good visual quality and computational effectiveness than other state-of-the-art works.

show abstract

Section: Modified Principal Component Analysis (Mpca)mentioning

confidence: 58%

Image Enhancement based on Fusion using 2D LPDCT and Modified PCA

2019

IJEAT

View full text Add to dashboard Cite

show abstract

“…It is concluded from the results shown in the table 1 to table 6 that the proposed FPDCT with modified PCA method provides better objectives than other state of the art PCA based algorithms. The statistical results obtained for different multifocus images has shown that the FPDCT algorithm shows superior performance as compared to other methods [24][25].…”

Section: Resultsmentioning

confidence: 96%

“…Recently Bin Yang et al [24] proposed both multi-focus image fusion and super-resolution algorithms via convolutional neural network (CNN). The main idea of this work is to learn the fusion weights using neural network technology to fuse the patches of multi-focus images.…”

Section: Introductionmentioning

confidence: 99%

Advanced Multifocus Image Fusion algorithm using FPDCT with Modified PCA

Mohan¹,

Kiran²,

Kumar³

2019

IJITEE

View full text Add to dashboard Cite

 Abstract: Image fusion has been performed and reported in this paper for multi-focused images using Frequency Partition Discrete Cosine Transform (FP-DCT) with Modified Principal component analysis (MPCA) technique. The image fusion with decomposition at fixed levels may be treated as a very critical rule in the earlier image processing techniques. The frequency partitioning approach was used in this study to select the decomposition levels based on the pixel intensity and clarity. This paper also presents the modified PCA technique which provides dimensionality reduction. The wide range of quality evaluation metrics was computed to compare the fusion performance on the five images. Different techniques such as PCA, wavelet transforms with PCA, Multiresolution Singular Value Decomposition (MSVD) with PCA, Multiresolution DCT (MRDCT) with PCA, Frequency partitioning DCT (FP-DCT) with PCA were computed for comparison with the proposed FP-DCT Modified PCA (MPCA) technique. Images obtained after fusion process obtained by the method proposed shows enhanced visual quality, negligible information loss and discontinuities in the image than compared to other state of the art methods.

show abstract

“…Zhen et al [34] proposed an algorithm to improve accurate contrast enhancement, but it has a poor effect on over-exposed regions. Some HDR algorithms [35][36][37] based on deep learning have been proposed to generate HDR images, but these algorithms do not solve the problems of camera shaking and object movement.…”

Section: Related Workmentioning

confidence: 99%

Anti-Shake HDR Imaging Using RAW Image Data

Liu

Huang

et al. 2020

Information

View full text Add to dashboard Cite

Camera shaking and object movement can cause the output images to suffer from blurring, noise, and other artifacts, leading to poor image quality and low dynamic range. Raw images contain minimally processed data from the image sensor compared with JPEG images. In this paper, an anti-shake high-dynamic-range imaging method is presented. This method is more robust to camera motion than previous techniques. An algorithm based on information entropy is employed to choose a reference image from the raw image sequence. To further improve the robustness of the proposed method, the Oriented FAST and Rotated BRIEF (ORB) algorithm is adopted to register the inputs, and a simple Laplacian pyramid fusion method is implanted to generate the high-dynamic-range image. Additionally, a large dataset with 435 various exposure image sequences is collected, which includes the corresponding JPEG image sequences to test the effectiveness of the proposed method. The experimental results illustrate that the proposed method achieves better performance in terms of anti-shake ability and preserves more details for real scene images than traditional algorithms. Furthermore, the proposed method is suitable for extreme-exposure image pairs, which can be applied to binocular vision systems to acquire high-quality real scene images, and has a lower algorithm complexity than deep learning-based fusion methods.

show abstract

Multi-focus image fusion and super-resolution with convolutional neural network

Cited by 38 publications

References 16 publications

Image Enhancement based on Fusion using 2D LPDCT and Modified PCA

Image Enhancement based on Fusion using 2D LPDCT and Modified PCA

Advanced Multifocus Image Fusion algorithm using FPDCT with Modified PCA

Anti-Shake HDR Imaging Using RAW Image Data

Contact Info

Product

Resources

About