Object recognition under bad weather conditions with wavelet-modified logarithmic fringe-adjusted joint transform correlator

Mohapatra, Jyoti Bikash; Monikantan, Jyothish; Nishchal, Naveen K.

doi:10.1007/s12596-024-02065-9

J Opt

2024

DOI: 10.1007/s12596-024-02065-9

|View full text |Cite

Object recognition under bad weather conditions with wavelet-modified logarithmic fringe-adjusted joint transform correlator

Jyoti Bikash Mohapatra,

Jyothish Monikantan,

Naveen K. Nishchal

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Object Recognition in Foggy and Hazy Conditions Using Dark Channel Prior-Based Fringe-Adjusted Joint Transform Correlator

Mohapatra,

Nishchal,

Monikantan

2024

Photonics

View full text Add to dashboard Cite

Extreme weather conditions like fog and haze present substantial challenges to object recognition systems. Reduced visibility and contrast degradation significantly affect the auto-correlation process, often leading to failure in object recognition. To address this critical issue and to make object recognition accurate and invincible, we propose a hybrid digital–optical correlator specifically designed to perform under adverse weather conditions. This approach integrates the dark channel prior (DCP) with the fringe-adjusted joint transform correlator (FJTC), promising significant potential to enhance the robustness of the object recognition process under challenging environmental conditions. The proposed scheme presents a unique and alternative approach for object recognition under bad weather conditions. The incoming input scenes are processed with the DCP, enabling the FJTC to perform optical correlation on the refined images. The effectiveness of the proposed method is evaluated using several performance metrics like the structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), correlation peak intensity (CPI), processing time, and recognition accuracy. To validate the performance of the proposed study, numerical simulation along with hybrid digital–optical demonstrations have been conducted.

show abstract

Object Recognition in Foggy and Hazy Conditions Using Dark Channel Prior-Based Fringe-Adjusted Joint Transform Correlator

Mohapatra,

Nishchal,

Monikantan

2024

Photonics

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Object recognition under bad weather conditions with wavelet-modified logarithmic fringe-adjusted joint transform correlator

Cited by 1 publication

References 47 publications

Object Recognition in Foggy and Hazy Conditions Using Dark Channel Prior-Based Fringe-Adjusted Joint Transform Correlator

Object Recognition in Foggy and Hazy Conditions Using Dark Channel Prior-Based Fringe-Adjusted Joint Transform Correlator

Contact Info

Product

Resources

About