Towards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy

Xu, Shuang; Ji, Lizhen; Wang, Zhe; Li, Pengfei; Sun, Kai; Zhang, Chunxia

doi:10.1109/tci.2020.3039564

Cited by 34 publications

(11 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For an unbiased evaluation of the fusion results, visual and objective evaluations were both adopted to compare our method with the other four representative fusion methods, which are the IFCNN, 21 MFF, 15 CNN 22 and RBI, 7 and their specific information is shown in Table 2. Six objective representative evaluation metrics were selected in this section, namely SD (calculated by Equations ( 6) and ( 7)), SSIM (obtained by Equations ( 8) and ( 9)), SF (obtained by Equation ( 11)), PSNR (calculated by Equations ( 13) and ( 14)), Q abf (obtained by Equations ( 15) and ( 16)) and AG (displayed in Equation ( 17)).…”

Section: Resultsmentioning

confidence: 99%

Spectral- and spatial-based multi-focus image fusion method towards multi-layer nonwovens

Zhu

Sun

et al. 2023

Textile Research Journal

View full text Add to dashboard Cite

In the microscopic imaging scenario where the object thickness exceeds the depth of field of the microscope, multi-focus image fusion (MFF) is an effective method to generate an all-in-focus image. However, for nonwoven fabric for which the captured image number is up to 100 or more, the existing methods often underperform in areas near the fiber edges, owing to image ghosting and noise accumulation caused by the platform moving. To address the above problem, this paper presents a method designed to fuse multi-layer micro-images based on the combination of spectral and spatial features of the images. Firstly, the spectral domain-based map is generated by decomposition and reconstruction of the high-frequency and low-frequency components of the images, aimed at obtaining the edge information. Simultaneously, the spatial domain-based fuse map is built through sharpness measurement, referring to visual perception. Finally, the two methods are combined via an optimized weight to obtain an all-in-focus fused image. Four groups of real-world data consisting of 100 multi-focus nonwoven images are utilized to verify the superiority of this method. The experimental results demonstrate that the proposed method can obtain satisfactory performance in terms of both human visual evaluation and objective evaluation compared with the image fusion framework based on the convolutional neural network, MFF, region-based image fusion algorithm and convolutional neural network state-of-the-art fusion methods.

show abstract

Section: Resultsmentioning

confidence: 99%

Spectral- and spatial-based multi-focus image fusion method towards multi-layer nonwovens

Zhu

Sun

et al. 2023

Textile Research Journal

View full text Add to dashboard Cite

show abstract

“… Ensemble of CNN for multi-focus image fusion [ 6 ] (ECNN). Towards reducing severe defocus spread effects for multi-focus image fusion via an optimization based strategy [ 5 ] (MFF-SSIM). MFF-GAN: An unsupervised generative adversarial network with adaptive and gradient joint constraints for multi-focus image fusion [ 4 ] (MFF-GAN).…”

Section: Methodsmentioning

confidence: 99%

“…Towards reducing severe defocus spread effects for multi-focus image fusion via an optimization based strategy [ 5 ] (MFF-SSIM).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Focus Image Fusion Based on Hessian Matrix Decomposition and Salient Difference Focus Detection

Wang

Cheng

et al. 2022

Entropy

View full text Add to dashboard Cite

Multi-focus image fusion integrates images from multiple focus regions of the same scene in focus to produce a fully focused image. However, the accurate retention of the focused pixels to the fusion result remains a major challenge. This study proposes a multi-focus image fusion algorithm based on Hessian matrix decomposition and salient difference focus detection, which can effectively retain the sharp pixels in the focus region of a source image. First, the source image was decomposed using a Hessian matrix to obtain the feature map containing the structural information. A focus difference analysis scheme based on the improved sum of a modified Laplacian was designed to effectively determine the focusing information at the corresponding positions of the structural feature map and source image. In the process of the decision-map optimization, considering the variability of image size, an adaptive multiscale consistency verification algorithm was designed, which helped the final fused image to effectively retain the focusing information of the source image. Experimental results showed that our method performed better than some state-of-the-art methods in both subjective and quantitative evaluation.

show abstract

“…Defocus blurring is inevitable when the scene regions (with wider depth range) are out-of-focus due to the limitation of the hardware, i.e., cameras with a finite-size aperture can only focus on the shadow depth of field (DoF) at a time, and the rest scene regions will contain blur [1]. Removing this blur and recovering defocused image details are challenging due to the spatially-varying point spread functions (PSFs) [2], [3], [4]. Recently, some studies address this problem by dual-pixel (DP) sensors found on most modern cameras [5].…”

Section: Introductionmentioning

confidence: 99%

BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurring

Liang,

Jiang,

Liu

et al. 2021

Preprint

View full text Add to dashboard Cite

The defocus deblurring raised from the finite aperture size and exposure time is an essential problem in the computational photography. It is very challenging because the blur kernel is spatially varying and difficult to estimate by traditional methods. Due to its great breakthrough in low-level tasks, convolutional neural networks (CNNs) have been introduced to the defocus deblurring problem and achieved significant progress. However, they apply the same kernel for different regions of the defocus blurred images, thus it is difficult to handle these nonuniform blurred images. To this end, this study designs a novel blur-aware multi-branch network (BaMBNet), in which different regions (with different blur amounts) should be treated differentially. In particular, we estimate the blur amounts of different regions by the internal geometric constraint of the DP data, which measures the defocus disparity between the left and right views. Based on the assumption that different image regions with different blur amounts have different deblurring difficulties, we leverage different networks with different capacities (i.e. parameters) to process different image regions. Moreover, we introduce a meta-learning defocus mask generation algorithm to assign each pixel to a proper branch. In this way, we can expect to well maintain the information of the clear regions while recovering the missing details of the blurred regions. Both quantitative and qualitative experiments demonstrate that our BaMBNet outperforms the state-of-the-art methods. Source code will be available at https://github.com/junjun-jiang/BaMBNet.

show abstract

Towards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy

Cited by 34 publications

References 35 publications

Spectral- and spatial-based multi-focus image fusion method towards multi-layer nonwovens

Spectral- and spatial-based multi-focus image fusion method towards multi-layer nonwovens

Multi-Focus Image Fusion Based on Hessian Matrix Decomposition and Salient Difference Focus Detection

BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurring

Contact Info

Product

Resources

About