7 research outputs found
Self-Supervised Spatially Variant PSF Estimation for Aberration-Aware Depth-from-Defocus
In this paper, we address the task of aberration-aware depth-from-defocus
(DfD), which takes account of spatially variant point spread functions (PSFs)
of a real camera. To effectively obtain the spatially variant PSFs of a real
camera without requiring any ground-truth PSFs, we propose a novel
self-supervised learning method that leverages the pair of real sharp and
blurred images, which can be easily captured by changing the aperture setting
of the camera. In our PSF estimation, we assume rotationally symmetric PSFs and
introduce the polar coordinate system to more accurately learn the PSF
estimation network. We also handle the focus breathing phenomenon that occurs
in real DfD situations. Experimental results on synthetic and real data
demonstrate the effectiveness of our method regarding both the PSF estimation
and the depth estimation
λμΌ ν½μ μ΄λ―Έμ§ κΈ°λ° μ λ‘μ· λν¬μ»€μ€ λλΈλ¬λ§
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Όλ¬Έ(μμ¬) -- μμΈλνκ΅λνμ : 곡과λν νλκ³Όμ μΈκ³΅μ§λ₯μ 곡, 2022. 8. ν보ν.Defocus deblurring in dual-pixel (DP) images is a challenging problem due to diverse camera optics and scene structures. Most of the existing algorithms rely on supervised learning approaches trained on the Canon DSLR dataset but often suffer from weak generalizability to out-of-distribution images including the ones captured by smartphones. We propose a novel zero-shot defocus deblurring algorithm, which only requires a pair of DP images without any training data and a pre-calibrated ground-truth blur kernel. Specifically, our approach first initializes a sharp latent map using a parametric blur kernel with a symmetry constraint. It then uses a convolutional neural network (CNN) to estimate the defocus map that best describes the observed DP image. Finally, it employs a generative model to learn scene-specific non-uniform blur kernels to compute the final enhanced images. We demonstrate that the proposed unsupervised technique outperforms the counterparts based on supervised learning when training and testing run in different datasets. We also present that our model achieves competitive accuracy when tested on in-distribution data.λμΌ ν½μ
(DP) μ΄λ―Έμ§ μΌμλ₯Ό μ¬μ©νλ μ€λ§νΈν°μμμ Defocus Blur νμμ λ€μν μΉ΄λ©λΌ κ΄ν ꡬ쑰μ 물체μ κΉμ΄ λ§λ€ λ€λ₯Έ νλ¦Ών¨ μ λλ‘ μΈν΄ μ μμ 볡μμ΄ μ½μ§ μμ΅λλ€. κΈ°μ‘΄ μκ³ λ¦¬μ¦λ€μ λͺ¨λ Canon DSLR λ°μ΄ν°μμ νλ ¨λ μ§λ νμ΅ μ κ·Ό λ°©μμ μμ‘΄νμ¬ μ€λ§νΈν°μΌλ‘ 촬μλ μ¬μ§μμλ μ μΌλ°νκ° λμ§ μμ΅λλ€. λ³Έ λ
Όλ¬Έμμλ νλ ¨ λ°μ΄ν°μ μ¬μ 보μ λ μ€μ Blur 컀λ μμ΄λ, ν μμ DP μ¬μ§λ§μΌλ‘λ νμ΅μ΄ κ°λ₯ν Zero-shot Defocus Deblurring μκ³ λ¦¬μ¦μ μ μν©λλ€. νΉν, λ³Έ λ
Όλ¬Έμμλ λμΉμ μΌλ‘ λͺ¨λΈλ§ λ Blur Kernelμ μ¬μ©νμ¬ μ΄κΈ° μμμ 볡μνλ©°, μ΄ν CNN(Convolutional Neural Network)μ μ¬μ©νμ¬ κ΄μ°°λ DP μ΄λ―Έμ§λ₯Ό κ°μ₯ μ μ€λͺ
νλ Defocus Mapμ μΆμ ν©λλ€. λ§μ§λ§μΌλ‘ CNNμ μ¬μ©νμ¬ μ₯λ©΄ λ³ Non-uniformν Blur Kernelμ νμ΅νμ¬ μ΅μ’
볡μ μμμ μ±λ₯μ κ°μ ν©λλ€. νμ΅κ³Ό μΆλ‘ μ΄ λ€λ₯Έ λ°μ΄ν° μΈνΈμμ μ€νλ λ, μ μλ λ°©λ²μ λΉμ§λ κΈ°μ μμλ λΆκ΅¬νκ³ μ΅κ·Όμ λ°νλ μ§λ νμ΅μ κΈ°λ°μ λ°©λ²λ€λ³΄λ€ μ°μν μ±λ₯μ 보μ¬μ€λλ€. λν νμ΅ λ κ²κ³Ό κ°μ λΆν¬ λ΄ λ°μ΄ν°μμ μΆλ‘ ν λλ μ§λ νμ΅ κΈ°λ°μ λ°©λ²λ€κ³Ό μ λμ λλ μ μ±μ μΌλ‘ λΉμ·ν μ±λ₯μ 보μ΄λ κ²μ νμΈν μ μμμ΅λλ€.1. Introduction 6
1.1. Background 6
1.2. Overview 9
1.3. Contribution 11
2. Related Works 12
2.1.Defocus Deblurring 12
2.2.Defocus Map 13
2.3.Multiplane Image Representation 14
2.4.DP Blur Kernel 14
3. Proposed Methods 16
3.1. Latent Map Initialization 17
3.2. Defocus Map Estimation 20
3.3. Learning Blur Kernel s 22
3.4. Implementation Details 25
4. Experiments 28
4.1. Dataset 28
4.2. Quantitative Results 29
4.3. Qualitative Results 31
5. Conclusions 37
5.1.Summary 37
5.2. Discussion 38μ