ANALISIS LITERATUR SISTEMATIS TERHADAP METODE IMAGE DENOISING BERBASIS DEEP LEARNING UNTUK COMPUTER VISION

Amelia Putri; Iin Karmila Septiani

doi:10.46576/syntax.v7i1.8865

ANALISIS LITERATUR SISTEMATIS TERHADAP METODE IMAGE DENOISING BERBASIS DEEP LEARNING UNTUK COMPUTER VISION

Amelia Putri, Iin Karmila Septiani

Abstract

ABSTRAK

Penggunaan citra digital dalam berbagai aplikasi Computer Vision seringkali terkendala oleh kehadiran noise yang menurunkan kualitas informasi visual. Makalah ini menyajikan Analisis Literatur Sistematis (SLR) terhadap perkembangan metode image denoising berbasis deep learning. Proses pencarian artikel dilakukan secara terstruktur melalui database yang terindeks Scopus dengan mengadaptasi protokol PRISMA. Melalui analisis terhadap 40 literatur kunci yang sepenuhnya bersumber dari database Scopus, ditemukan pergeseran signifikan dari metode yang membutuhkan data bersih (supervised) menuju pendekatan yang lebih fleksibel seperti Noise2Noise dan blind denoising untuk menangani noise pada dunia nyata. Hasil tinjauan ini memberikan gambaran komprehensif mengenai tren arsitektur, dataset benchmark, serta tantangan dalam mencapai efisiensi komputasi untuk restorasi citra resolusi tinggi.

Kata kunci— Image Denoising, Deep Learning, Systematic Literature Review, Computer Vision, Scopus, PRISMA.

ABSTRACT

The use of digital imagery in various Computer Vision applications is often hindered by the presence of noise, which degrades visual information quality. This paper presents a Systematic Literature Review (SLR) on the development of deep learning-based image denoising methods. The article search process was structured through Scopus-indexed databases using the PRISMA protocol. Through an analysis of 40 key literatures completely sourced from the Scopus database, a significant shift was identified from supervised methods requiring clean data toward more flexible approaches, such as Noise2Noise and blind denoising, to handle real-world noise. The results of this review provide a comprehensive overview of architectural trends, benchmark datasets, and the challenges in achieving computational efficiency for high-resolution image restoration.

Keyword— Image Denoising, Deep Learning, Systematic Literature Review, Computer Vision, Scopus, PRISMA.

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DOI: https://doi.org/10.46576/syntax.v7i1.8865