Image Reconstruction via Manifold Constrained Convolutional Sparse Coding for Image Sets

Convolution sparse coding (CSC) has attracted much attention recently due to its advantages in image reconstruction and enhancement. However, the coding process suffers from perturbations caused by variations of input samples, as the consistence of features from similar input samples are not well ad...

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Vydáno v:IEEE journal of selected topics in signal processing Ročník 11; číslo 7; s. 1072 - 1081
Hlavní autoři: Yang, Linlin, Li, Ce, Han, Jungong, Chen, Chen, Ye, Qixiang, Zhang, Baochang, Cao, Xianbin, Liu, Wanquan
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.10.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Constraints
Convolution
Convolutional codes
Convolutional sparse coding
Data structures
Feature extraction
Image coding
image deblurring
Image enhancement
Image reconstruction
Image restoration
light field
Light fields
manifold constrained convolutional sparse coding
Manifolds
Robustness (mathematics)
State of the art
ISSN:1932-4553, 1941-0484
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Abstract Convolution sparse coding (CSC) has attracted much attention recently due to its advantages in image reconstruction and enhancement. However, the coding process suffers from perturbations caused by variations of input samples, as the consistence of features from similar input samples are not well addressed in the existing literature. In this paper, we will tackle this feature consistence problem from a set of samples via a proposed manifold constrained convolutional sparse coding (MCSC) method. The core idea of MCSC is to use the intrinsic manifold (Laplacian) structure of the input data to regularize the traditional CSC such that the consistence between features extracted from input samples can be well preserved. To implement the proposed MCSC method efficiently, the alternating direction method of multipliers (ADMM) approach is employed, which can consistently integrate the underlying Laplacian constraints during the optimization process. With this regularized data structure constraint, the MCSC can achieve a much better solution which is robust to the variance of the input samples against overcomplete filters. We demonstrate the capacity of MCSC by providing the state-of-the-art results when applied it to the task of reconstructing light fields. Finally, we show that the proposed MCSC is a generic approach as it also achieves better results than the state-of-the-art approaches based on convolutional sparse coding in other image reconstruction tasks, such as face reconstruction, digit reconstruction, and image restoration.
AbstractList Convolution sparse coding (CSC) has attracted much attention recently due to its advantages in image reconstruction and enhancement. However, the coding process suffers from perturbations caused by variations of input samples, as the consistence of features from similar input samples are not well addressed in the existing literature. In this paper, we will tackle this feature consistence problem from a set of samples via a proposed manifold constrained convolutional sparse coding (MCSC) method. The core idea of MCSC is to use the intrinsic manifold (Laplacian) structure of the input data to regularize the traditional CSC such that the consistence between features extracted from input samples can be well preserved. To implement the proposed MCSC method efficiently, the alternating direction method of multipliers (ADMM) approach is employed, which can consistently integrate the underlying Laplacian constraints during the optimization process. With this regularized data structure constraint, the MCSC can achieve a much better solution which is robust to the variance of the input samples against overcomplete filters. We demonstrate the capacity of MCSC by providing the state-of-the-art results when applied it to the task of reconstructing light fields. Finally, we show that the proposed MCSC is a generic approach as it also achieves better results than the state-of-the-art approaches based on convolutional sparse coding in other image reconstruction tasks, such as face reconstruction, digit reconstruction, and image restoration.
Author Chen Chen
Baochang Zhang
Xianbin Cao
Ce Li
Qixiang Ye
Jungong Han
Linlin Yang
Wanquan Liu
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SubjectTerms Constraints
Convolution
Convolutional codes
Convolutional sparse coding
Data structures
Feature extraction
Image coding
image deblurring
Image enhancement
Image reconstruction
Image restoration
light field
Light fields
manifold constrained convolutional sparse coding
Manifolds
Robustness (mathematics)
State of the art
Title Image Reconstruction via Manifold Constrained Convolutional Sparse Coding for Image Sets
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