R2C: improvingab initioresidue contact map prediction using dynamic fusion strategy and Gaussian noise filter

A novel method was brought forward for the purpose of filtering Gaussian noise effectively by using variable step time matrix of the simplified pulse coupled neural network (PCNN). Firstly, the time matrix of PCNN, related to the grayscale and spatial information of an image, is calculated to identify the noise polluted pixels. Subsequently, a variable step, a long step for strong noise and a short step for weak noise, based on the time matrix is applied to modify the grayscale of noised pixels in a sliding window. And then wiener filter is used to the image to further filter the noise. Experiments show that the proposed filter can remove Gaussian noise effectively than other noise reduction methods such as median filter, mean filter, wiener filter etc, and the filtered image is smooth and the details and edges are sharp. Compared with existing PCNN based Gaussian noise filter, the proposed filter gets higher Peak Signal-to-Noise Ratio (PSNR) and better performance.

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Perbaikan Citra Digital dengan Menggunakan Filtering Technique dan Similarity Measurement

d'CARTESIAN ◽

10.35799/dc.2.2.2013.3203 ◽

2013 ◽

Vol 2 (2) ◽

pp. 1

Author(s):

Gybert Saselah ◽

Winsy Weku ◽

Luther Latumakulita

Keyword(s):

Digital Image ◽

Gaussian Noise ◽

Digital Images ◽

Wiener Filter ◽

Reduction Process ◽

Input Image ◽

Similarity Measurement ◽

Gaussian Filter ◽

Original Image ◽

Noise Filter

Abstract Often the digital image can be contaminated with noise, which usually occurs in the process of retrieval or storage of digital images and delivery process either via satellite or cable . By using the technique of filtering noise reduction process will be performed on a digital image that has previously been given Gaussian noise and followed by a Similarity Measurement to identify similarities between image filtered and original image. This study was conducted to determine the appropriate filtering techniques to reduce the Gaussian noise. Image processing in this study composed by the input image and read the image matrix, converting images, adding noise, denoising digital images by applying filters performed using Matlab R2012a software ( version 7.14.0.739) . Application of Gaussian filter with a value of = 1.0 produce a digital image that is closest to the original image than the application of a Gaussian filter with another value, for . As for the application of the Wiener filter is seen that the greater the value, the resulting digital image will be closer to the original image. For further research can be done on other types of noise or to a combination of two or more noise. Keywords : Digital Image , Noise , Filter , Similarity Measurement. Abstrak Seringkali citra digital dapat terkontaminasi derau (noise), yang biasanya terjadi pada proses pengambilan ataupun penyimpanan citra digital serta proses pengiriman citra digital baik melalui satelit maupun melalui kabel juga. Dengan menggunakan teknik filtering akan dilakukan proses pengurangan noise pada suatu citra digital yang sebelumnya telah diberi Gaussian noise dan dilanjutkan dengan Similarity Measurement untuk mengidentifikasi kesamaan citra digital hasil filtering dengan citra original. Penelitian ini dilakukan untuk menentukan teknik filtering yang tepat untuk mengurangi Gaussian noise. Proses pengolahan citra dalam penelitian ini terdiri dengan proses input gambar dan membaca matriks citra, konversi citra, menambahkan noise, denoising citra digital dengan menerapkan filter yang dilakukan dengan menggunakan software Matlab R2012a (versi 7.14.0.739). Penerapan Gaussian filter dengan nilai = 1,0 menghasilkan citra digital yang paling mendekati citra original dibandingkan dengan penerapan Gaussian filter dengan nilai lain, dimana . Sedangkan untuk penerapan Wiener filter terlihat bahwa semakin besar nilai , maka citra digital yang dihasilkan akan semakin mendekati citra original. Untuk penelitian selanjutnya dapat dilakukan pada jenis noise lain ataupun untuk gabungan dua noise atau lebih. Kata kunci: Citra digital, Noise, Filter, Similarity Measurement

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Gaussian noise filter based on PCNN

International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003 ◽

10.1109/icnnsp.2003.1279233 ◽

2003 ◽

Cited By ~ 6

Author(s):

Ma Yi-de ◽

Shi Fei ◽

Li Lian

Keyword(s):

Gaussian Noise ◽

Noise Filter

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Low Distortion of Noise Filter Realization with 6.34 V/μs Fast Slew Rate and 120 mVp-p Output Noise Signal

Sensors ◽

10.3390/s21031008 ◽

2021 ◽

Vol 21 (3) ◽

pp. 1008

Author(s):

Fang-Ming Yu ◽

Kun-Cheng Lee ◽

Ko-Wen Jwo ◽

Rong-Seng Chang ◽

Jun-Yi Lin

Keyword(s):

Input Signal ◽

Gaussian Noise ◽

Fast Response ◽

Noise Signal ◽

High Order ◽

Phase Delay ◽

Slew Rate ◽

Noise Filter ◽

Output Noise ◽

Actual Signal

In order to reduce Gaussian noise, this paper proposes a method via taking the average of the upper and lower envelopes generated by capturing the high and low peaks of the input signal. The designed fast response filter has no cut-off frequency, so the high order harmonics of the actual signal remain unchanged. Therefore, it can immediately respond to the changes of input signal and retain the integrity of the actual signal. In addition, it has only a small phase delay. The slew rate, phase delay and frequency response can be confirmed from the simulation results of Multisim 13.0. The filter outlined in this article can retain the high order harmonics of the original signal, achieving a slew rate of 6.34 V/μs and an almost zero phase difference. When using our filter to physically test the input signal with a noise level of 3 Vp-p Gaussian noise, a reduced noise signal of 120 mVp-p is obtained. The noise can be suppressed by up to 4% of the raw signal.

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