Physically inspired depth-from-defocus

Persch, Nico; Schroers, Christopher; Setzer, Simon; Weickert, Joachim

doi:10.1016/j.imavis.2016.08.011

Cited by 10 publications

(5 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A closely related problem, known as depth-from-defocus, is to estimate the distance between an object and the image acquisition device from a whole image stack (rather than just one image as in our case) that was generated by capturing the object under varying focal settings. The forward model described by Persch et al [20], which mimics the image acquisition of a thin lens camera, is comparable to our 2D microscope model. Likewise related is the article by Aguet et al [1], which suggests a method of how an image stack, produced by moving a sample through the different focal planes of a 2D microscope, can be combined into a single feature enriched image.…”

Section: Introductionmentioning

confidence: 80%

Elastic 3D-2D Image Registration

Striewski¹,

Wirth²

2021

Preprint

View full text Add to dashboard Cite

We propose a method to non-rigidly align a three-dimensional (3D) volumetric image with a two-dimensional (2D) planar image representing a projection of the deformed volume. The application in mind comes from biological studies in which 2D intravital microscopy videos of living tissue are recorded, after which the tissue is excised and a more detailed 3D volume microscopy is performed. Coregistration of both data sets allows to combine the temporal (but 2D) information with more detailed spatial 3D information.Our approach is variational and uses a hyperelastic deformation regularization, as is appropriate for biological material. As a particular feature, the out of plane deformation is estimated based on the out of focus blur inside the 2D microscopy image. The approach becomes computationally feasible through the use of a coarse-to-fine optimization strategy and higher order optimization methods.

show abstract

Section: Introductionmentioning

confidence: 80%

Elastic 3D-2D Image Registration

Striewski¹,

Wirth²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…PSF Modelling: Most approaches assume a convolutional formation model, allowing the PSF to be approximated as a 2D kernel. Two popular choices include the Pillbox [65,18] and Gaussian [20,4,51] functions. These methods do not consider many of the abberations present in optical systems, so some works [31,44] instead directly measure the blurring response of the camera.…”

Section: Depth From Defocusmentioning

confidence: 99%

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

2021

View full text Add to dashboard Cite

While the accuracy of multi-view stereo (MVS) has continued to advance, its performance reconstructing challenging scenes from images with a limited depth of field is generally poor. Typical implementations assume a pinhole camera model, and therefore treat defocused regions as a source of outlier. In this paper, we address these limitations by instead modelling the camera as a thick lens. Doing so allows us to exploit the complementary nature of stereo and defocus information, and overcome constraints imposed by traditional MVS methods. Using our novel reconstruction framework, we recover complete 3D models of complex macro-scale scenes. Our approach demonstrates robustness to view-dependent materials, and outperforms stateof-the-art MVS and depth from defocus across a range of real and synthetic datasets.

show abstract

“…Persch et al [14] proposed a variational approach for the problem of depth from defocus based on modeling of the image formation by featuring the thin lens model and preserving the crucial physical properties such as maximum-minimum principle for the intensity values. Later, the variational model is minimized using the multiplicative Euler-Lagrange.…”

Section: Previous Workmentioning

confidence: 99%

“…Later, the variational model is minimized using the multiplicative Euler-Lagrange. The proposed solution in [14] appears to generate false depth levels in relatively close scenes and in general, the depth profiles are likely to be affected by the color information as the robustification method employed in [14] uses the full-color information of the focal stack.…”

Section: Previous Workmentioning

confidence: 99%

Application of preconditioned alternating direction method of multipliers in depth from focal stack

Javidnia

Corcoran

2018

J. Electron. Imag.

View full text Add to dashboard Cite

Post capture refocusing effect in smartphone cameras is achievable by using focal stacks. However, the accuracy of this effect is totally dependent on the combination of the depth layers in the stack. The accuracy of the extended depth of field effect in this application can be improved significantly by computing an accurate depth map which has been an open issue for decades. To tackle this issue, in this paper, a framework is proposed based on Preconditioned Alternating Direction Method of Multipliers (PADMM) for depth from the focal stack and synthetic defocus application. In addition to its ability to provide high structural accuracy and occlusion handling, the optimization function of the proposed method can, in fact, converge faster and better than state of the art methods. The evaluation has been done on 21 sets of focal stacks and the optimization function has been compared against 5 other methods. Preliminary results indicate that the proposed method has a better performance in terms of structural accuracy and optimization in comparison to the current state of the art methods.

show abstract

Physically inspired depth-from-defocus

Cited by 10 publications

References 37 publications

Elastic 3D-2D Image Registration

Elastic 3D-2D Image Registration

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)

Application of preconditioned alternating direction method of multipliers in depth from focal stack

Contact Info

Product

Resources

About