Evidence map›Paper›PMID 29649114›Full record

ArticleSensors (Basel, Switzerland)2018

Optimized Multi-Spectral Filter Array Based Imaging of Natural Scenes.

Yuqi Li, Aditi Majumder, Hao Zhang, M Gopi

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Design of a Dual-Mode Multispectral Filter Array.Sensors (Basel, Switzerland) · 2023
    Article
  2. Towards 'Fourth Paradigm' Spectral Sensing.Sensors (Basel, Switzerland) · 2022
    Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yuqi LiCollege of Information Science and Engineering, Ningbo University, Ningbo 315000, China. liyuqi1@nbu.edu.cn.
Aditi MajumderDepartment of Computer Science, University of California, Irvine, CA 92697-3425, USA. majumder@ics.uci.edu.
Hao ZhangDepartment of Computer Science, University of California, Irvine, CA 92697-3425, USA. zhang.hao.alan@gmail.com.
M GopiDepartment of Computer Science, University of California, Irvine, CA 92697-3425, USA. gopi@ics.uci.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multi-spectral imaging using a camera with more than three channels is an efficient method to acquire and reconstruct spectral data and is used extensively in tasks like object recognition, relighted rendering, and color constancy. Recently developed methods are used to only guide content-dependent filter selection where the set of spectral reflectances to be recovered are known a priori. We present the first content-independent spectral imaging pipeline that allows optimal selection of multiple channels. We also present algorithms for optimal placement of the channels in the color filter array yielding an efficient demosaicing order resulting in accurate spectral recovery of natural reflectance functions. These reflectance functions have the property that their power spectrum statistically exhibits a power-law behavior. Using this property, we propose power-law based error descriptors that are minimized to optimize the imaging pipeline. We extensively verify our models and optimizations using large sets of commercially available wide-band filters to demonstrate the greater accuracy and efficiency of our multi-spectral imaging pipeline over existing methods.

Indexed as

content independent channel selectiondemosaicingmultispectral filter arraymulti-spectral imaging

Identifiers

PMID29649114
PMCPMC5948481

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.