Block-based Compressive Low-light-level Imaging

被引：0

作者：

Ke, Jun ^{[1
]}

Wei, Ping ^{[1
]}

Zhang, Xin ^{[2
]}

Lam, Edmund Y. ^{[3
]}

机构：

[1] Beijing Inst Technol, Sch Optoelect, Minist Educ China, Key Lab Photoelect Imaging Technol & Syst, Beijing 100081, Peoples R China

[2] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China

[3] Univ Hong Kong, Dept Elect & Elect Engn, Pokfulam, Hong Kong, Peoples R China

来源：

2013 IEEE INTERNATIONAL CONFERENCE ON IMAGING SYSTEMS AND TECHNIQUES (IST 2013) | 2013年

关键词：

RECONSTRUCTION;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, block-based compressive low-light-level imaging (BCL-imaging) is studied. To obtain larger measurement SNR (signal to noise ratio), instead of object pixels, linear combinations of pixels, referred to as features, are collected. PCA and Hadamard features are studied. Measurement SNR and reconstruction error are analyzed to quantify BCL-imaging performance. Compared with conventional imaging, BCL-imaging presents better reconstruction quality. Between PCA and Hadamard projections, PCA has smaller reconstruction error. However, after sorting the projection vectors using measurement SNR, Hadamard can obtain similarly performance as PCA. Biased vector and dual-measurements are studied with experimental results for the implementation of both projections in the end of this paper.

引用

页码：311 / 316

页数：6

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