35 research outputs found
๋น๊ฐ์ฐ์์ ์ก์ ์์ ๋ณต์์ ์ํ ๊ทธ๋ฃน ํฌ์ ํํ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :์์ฐ๊ณผํ๋ํ ์๋ฆฌ๊ณผํ๋ถ,2020. 2. ๊ฐ๋ช
์ฃผ.For the image restoration problem, recent variational approaches exploiting nonlocal information of an image have demonstrated significant improvements compared with traditional methods utilizing local features. Hence, we propose two variational models based on the sparse representation of image groups, to recover images with non-Gaussian noise. The proposed models are designed to restore image with Cauchy noise and speckle noise, respectively. To achieve efficient and stable performance, an alternating optimization scheme with a novel initialization technique is used. Experimental results suggest that the proposed methods outperform other methods in terms of both visual perception and numerical indexes.์์ ๋ณต์ ๋ฌธ์ ์์, ์์์ ๋น๊ตญ์ง์ ์ธ ์ ๋ณด๋ฅผ ํ์ฉํ๋ ์ต๊ทผ์ ๋ค์ํ ์ ๊ทผ ๋ฐฉ์์ ๊ตญ์ง์ ์ธ ํน์ฑ์ ํ์ฉํ๋ ๊ธฐ์กด ๋ฐฉ๋ฒ๊ณผ ๋น๊ตํ์ฌ ํฌ๊ฒ ๊ฐ์ ๋์๋ค. ๋ฐ๋ผ์, ์ฐ๋ฆฌ๋ ๋น๊ฐ์ฐ์์ ์ก์ ์์์ ๋ณต์ํ๊ธฐ ์ํด ์์ ๊ทธ๋ฃน ํฌ์ ํํ์ ๊ธฐ๋ฐํ ๋ ๊ฐ์ง ๋ณ๋ถ๋ฒ์ ๋ชจ๋ธ์ ์ ์ํ๋ค. ์ ์๋ ๋ชจ๋ธ์ ๊ฐ๊ฐ ์ฝ์ ์ก์๊ณผ ์คํํด ์ก์ ์์์ ๋ณต์ํ๋๋ก ์ค๊ณ๋์๋ค. ํจ์จ์ ์ด๊ณ ์์ ์ ์ธ ์ฑ๋ฅ์ ๋ฌ์ฑํ๊ธฐ ์ํด, ๊ต๋ ๋ฐฉํฅ ์น์๋ฒ๊ณผ ์๋ก์ด ์ด๊ธฐํ ๊ธฐ์ ์ด ์ฌ์ฉ๋๋ค. ์คํ ๊ฒฐ๊ณผ๋ ์ ์๋ ๋ฐฉ๋ฒ์ด ์๊ฐ์ ์ธ ์ธ์๊ณผ ์์น์ ์ธ ์งํ ๋ชจ๋์์ ๋ค๋ฅธ ๋ฐฉ๋ฒ๋ณด๋ค ์ฐ์ํจ์ ๋ํ๋ธ๋ค.1 Introduction 1
2 Preliminaries 5
2.1 Cauchy Noise 5
2.1.1 Introduction 6
2.1.2 Literature Review 7
2.2 Speckle Noise 9
2.2.1 Introduction 10
2.2.2 Literature Review 13
2.3 GSR 15
2.3.1 Group Construction 15
2.3.2 GSR Modeling 16
2.4 ADMM 17
3 Proposed Models 19
3.1 Proposed Model 1: GSRC 19
3.1.1 GSRC Modeling via MAP Estimator 20
3.1.2 Patch Distance for Cauchy Noise 22
3.1.3 The ADMM Algorithm for Solving (3.7) 22
3.1.4 Numerical Experiments 28
3.1.5 Discussion 45
3.2 Proposed Model 2: GSRS 48
3.2.1 GSRS Modeling via MAP Estimator 50
3.2.2 Patch Distance for Speckle Noise 52
3.2.3 The ADMM Algorithm for Solving (3.42) 53
3.2.4 Numerical Experiments 56
3.2.5 Discussion 69
4 Conclusion 74
Abstract (in Korean) 84Docto
Deep Model-Based Super-Resolution with Non-uniform Blur
We propose a state-of-the-art method for super-resolution with non-uniform
blur. Single-image super-resolution methods seek to restore a high-resolution
image from blurred, subsampled, and noisy measurements. Despite their
impressive performance, existing techniques usually assume a uniform blur
kernel. Hence, these techniques do not generalize well to the more general case
of non-uniform blur. Instead, in this paper, we address the more realistic and
computationally challenging case of spatially-varying blur. To this end, we
first propose a fast deep plug-and-play algorithm, based on linearized ADMM
splitting techniques, which can solve the super-resolution problem with
spatially-varying blur. Second, we unfold our iterative algorithm into a single
network and train it end-to-end. In this way, we overcome the intricacy of
manually tuning the parameters involved in the optimization scheme. Our
algorithm presents remarkable performance and generalizes well after a single
training to a large family of spatially-varying blur kernels, noise levels and
scale factors
CT Image Reconstruction by Spatial-Radon Domain Data-Driven Tight Frame Regularization
This paper proposes a spatial-Radon domain CT image reconstruction model
based on data-driven tight frames (SRD-DDTF). The proposed SRD-DDTF model
combines the idea of joint image and Radon domain inpainting model of
\cite{Dong2013X} and that of the data-driven tight frames for image denoising
\cite{cai2014data}. It is different from existing models in that both CT image
and its corresponding high quality projection image are reconstructed
simultaneously using sparsity priors by tight frames that are adaptively
learned from the data to provide optimal sparse approximations. An alternative
minimization algorithm is designed to solve the proposed model which is
nonsmooth and nonconvex. Convergence analysis of the algorithm is provided.
Numerical experiments showed that the SRD-DDTF model is superior to the model
by \cite{Dong2013X} especially in recovering some subtle structures in the
images