Image of KLASIFIKASI DUA TAHAP PADA IMAGE DEBLURRING WAJAH BERDASARKAN JENIS DAN TINGKAT KEPARAHAN BLUR DENGAN CONVOLUTIONAL NEURAL NETWORKS DAN U-NET

Skripsi

KLASIFIKASI DUA TAHAP PADA IMAGE DEBLURRING WAJAH BERDASARKAN JENIS DAN TINGKAT KEPARAHAN BLUR DENGAN CONVOLUTIONAL NEURAL NETWORKS DAN U-NET

Penilaian

0,0

dari 5
Penilaian anda saat ini :  

Image blur, particularly in facial images with varying types and severity levels, can significantly degrade visual quality and hinder the performance of facial recognition systems. This study proposes a two-stage classification approach for image deblurring based on Convolutional Neural Networks (CNN) and U-Net. The approach separates the classification of blur type and blur severity before applying a specialized deblurring model tailored for each identified combination. The three main blur types addressed are Gaussian Blur, Motion Blur, and Average Blur, each divided into five severity levels. Each deblurring model is independently developed according to the identified blur category through the two-stage classification system. Experimental results show that this separated approach significantly improves image restoration quality especially at low to moderate severity levels compared to conventional methods that use a single model for all severity levels. The average PSNR and SSIM scores, which reached 30.946 and 0.898 respectively, confirm the effectiveness of this strategy. Furthermore, the proposed framework enables more adaptive and specific processing tailored to the characteristics of the blurred image. In conclusion, integrating two-stage classification with deblurring model separation based on blur type and severity has been shown to enhance overall image restoration performance.

Availability
Inventory Code Barcode Call Number Location Status
2507004244T179282T1792822025Central Library (Reference)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1792822025
Publisher
Palembang : Program Magister Ilmu Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xiii, 97 hlm.; ilus.; tab.; 29 cm.
Language
Indonesia
ISBN/ISSN
-
Classification
004.07
Content Type
Text
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Ilmu Komputer
Specific Detail Info
-
Statement of Responsibility
Other version/related

No other version available

File Attachment
Comments
You must be logged in to post a comment