Image Reconstruction Via Manifold Constrained Convolutional Sparse Coding For Image Sets

Keywords

Convolutional sparse coding; image deblurring; image reconstruction; light field; manifold constrained convolutional sparse coding

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.

Publication Date

10-1-2017

Publication Title

IEEE Journal on Selected Topics in Signal Processing

Volume

11

Issue

7

Number of Pages

1072-1081

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/JSTSP.2017.2743683

Socpus ID

85028510948 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85028510948

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