A soft weighted median filter for removing general purpose image noise

Manek, Siprianus Septian; Tjandrasa, Handayani

doi:10.1109/icts.2017.8265601

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…Terdapat banyak metode untuk proses noise filtering seperti: New Adaptive Weighted Mean Filter (A-WMF) [1], Unbiased Weighted Mean Filter (U-WMF) [2], Interpolation Based Impulse Noise Removal (IBINR) [3], dan Weighted Mean Filter for removal of High Density Noise (HD-WMF) [4], semua metode tersebut dibuat hanya untuk menangani fixed valued noise (FVN). Oleh karena itu dibuat metode Soft Weighted Median Filter (SWMF) [5] untuk fixed valued noise (FVN) maupun random valued noise (RVN). Adapun metode-metode yang lain yang juga dapat menangani FVN N maupun RVN dan sering digunakan seperti: Median Filter, Mean Filter, Wiener Filter, dan Gaussian Filter [6].…”

Section: Metode Soft Weighted Median Filter Untuk Perbaikan Segmentasunclassified

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Soft Weighted Median Filter Method for Improved Image Segmentation with Noise

Manek

Tjandrasa

2018

JUTI

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2) ABSTRAK Metode Soft Weighted Median Filter (SWMF) merupakan salah satu metode noise filtering baru dalam image processing. Metode ini digunakan untuk menangani dua tipe noise pada citra yaitu fixed valued noise (FVN) dan random valued noise (RVN). Fixed valued noise adalah tipe noise dengan nilai yang tidak berubah (tetap), noise ini mengganti nilai piksel pada citra menjadi nilai maksimum dan minimum (0 dan 255), sedangkan random valued noise adalah tipe noise dengan nilai yang berubah-ubah (acak). Contoh untuk fixed valued noise seperti: salt&pepper noise, sedangkan untuk random valued noise seperti: gaussian, poisson, speckle, dan localvar noise. Berdasarkan penelitian sebelumnya, metode SWMF dapat diterapkan pada semua citra dengan semua jenis noise (FVN maupun RVN) serta mampu mengurangi noise tersebut dengan baik. Metode ini memiliki nilai PSNR yang lebih tinggi dibandingkan dengan metode lain, terutama untuk random valued noise seperti: gaussian, speckle, dan localvar noise. Pada penelitian ini, kami mengusulkan untuk meneliti kinerja dari metode SWMF lebih lanjut yaitu dengan membandingkan metode ini dengan metode lain seperti: Median Filter, Mean Filter, Gaussian Filter, dan Wiener Filter pada proses segmentasi citra. Proses segmentasi citra pada penelitian ini berdasarkan deteksi area menggunakan Top-Hat transform dan Otsu thresholding dan deteksi garis menggunakan Sobel edge detection. Proses pengukuran kinerja menggunakan perhitungan nilai sensitivity, specificity, dan accuracy pada citra hasil segmentasi dengan citra groundtruh. Hasil penelitian menunjukkan bahwa metode Soft Weighted Median Filter mampu meningkatkan kualitas segmentasi citra dengan rata-rata accuracy sebesar 95,70% dengan mengurangi fixed value noise maupun random valued noise pada citra. Sedangkan sensitivity dan specificity rata-rata memiliki nilai sebesar 90,48% dan 97,98%.

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Section: Metode Soft Weighted Median Filter Untuk Perbaikan Segmentasunclassified

“…Metode SWMF [5] ini merupakan gabungan dari Mean Filter [1], [4] dan Median Filter dimana proses pembobotan untuk setiap nilai pikselnya menggunakan fungsi perpangkatan, metode ini memberikan nilai bobot yang lebih tinggi pada nilai tengah namun tetap memperhitungkan nilai piksel tetangga untuk mendapatkan nilai piksel baru.…”

Section: Soft Weighted Median Filter [Swmf]unclassified

Soft Weighted Median Filter Method for Improved Image Segmentation with Noise

Manek

Tjandrasa

2018

JUTI

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Hybrid Denoising Development to Improve the Quality of Image Segmentation with Noise

Meidyani

Tjandrasa

2019

2019 12th International Conference on Information &Amp; Communication Technology and System (ICTS)

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3D22MX: Performance Subjective Evaluation of 3D/Stereoscopic Image Processing and Analysis

et al. 2022

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This work is divided into three parts: (i) a methodology developed for building a 3D/ stereoscopic database, called 3D22MX, (ii) a software tool designed for degradation of 3D/stereoscopic images, and (iii) a psychophysical experiment carried out for a specific type of noise. The novelty of this work is to integrate these three parts precisely to provide not only professionals who design algorithms to estimate three-dimensional image quality but also those who wish to generate new image databases. For the development of the 3D/stereoscopic database, 15 indoor images and 5 outdoor ones were spatial-calibrated and lighted for different types of scenarios. Criteria calibration is different for indoor images with respect to outdoor images. The software tool to degrade 3D/stereoscopic images is designed from MatLab programming language since images captured in the first part are calculated for achieving several image degradation. Our program has ten different types of noises for degradation, such as white Gauss impulse, localvar, spatial correlation, salt & pepper, speckle, blur, contrast, jpeg, and j2k. Due to each type of noise containing up to five levels of degradation, in this proposal, a database of 20 images is required to design a tool for degrading and generating three-dimensional images ranging from all types of noise to yield psychophysical. Finally, there are applied specific criteria to carry out some psychophysical experiments with 3D/stereoscopic images. Moreover, we analyzed the methodology used to qualify and apply images to the j2k noise, explaining every degradation level for this noise.

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A soft weighted median filter for removing general purpose image noise

Cited by 3 publications

References 3 publications

Soft Weighted Median Filter Method for Improved Image Segmentation with Noise

Soft Weighted Median Filter Method for Improved Image Segmentation with Noise

Hybrid Denoising Development to Improve the Quality of Image Segmentation with Noise

3D22MX: Performance Subjective Evaluation of 3D/Stereoscopic Image Processing and Analysis

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