• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.6
• /
• pp.1167-1173
• /
• 2017

As the society increasingly embraces the high - tech digital information age, the field of image processing becomes progressively more branched out and becoming an imperative field. However, image data is deteriorated due to various causes during transmission and salt and pepper noise is typical. Typical methods for removing salt and pepper noise include CWMF, SWMF, and A-TMF. However, existing methods are somewhat insufficient in their ability to remove noise in salt and pepper noise environments. Therefore, in this paper, after it is determined whether noise removal is needed, the following measures were taken. If the center pixel was non-noise, the original pixel was preserved, If it was noise, we proposed a two - dimensional spline interpolation method and a median filter depending on the noise density of the local mask. For the purpose of objective judgment, we compared the results with that of existing methods and used PSNR (peak signal to noise ratio) as a judgment criterion.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.7
• /
• pp.1383-1388
• /
• 2016

Although image signal processing is used in many fields, degradation takes place in the process of transmitting image data by several causes. CWMF, A-TMF, and AWMF are the typical methods to eliminate noises from image data damaged under salt and pepper noise environment. However, those filters are not effective for noise rejection under highly dense noise environment. In this respect, the present study proposed an algorithm to remove in salt and pepper noise. In case the center pixel is determined to be non-noise, it is replaced with original pixel. In case the center pixel is noise, it segments local mask into 4 directions and uses linear interpolation to estimate original pixel. And then it applies spatial weight to the estimated pixel. The proposed algorithm shows a high PSNR of 24.56[dB] for House images that had been damaged of salt and pepper noise(P = 50%), compared to the existing CWMF, A-TMF and AWMF there were improvements by 16.46[dB], 12.28[dB], and 12.32[dB], respectively.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.10
• /
• pp.1955-1960
• /
• 2016

Currently, with the rapid development in digital era, the image equipment related to multi-media is becoming commercialized. However, in the process of transmitting image data, deterioration occurs due to various causes, and the most representative deterioration is salt and pepper noise. There are many methods of eliminating salt and pepper noise such as SWMF, RSIF, MNRF, which are rather insufficient in eliminating noise in high-density slat and pepper noise environment. Therefore, in order to eliminate salt and pepper noise, this thesis proposes an algorithm by first judging the noise, and when the center pixel value is non-noise, the original pixel is preserved, and when it is noise, the partial mask is subdivided into 4 directions to apply cubic spline interpolation to the direction with most non-noise pixels. Also, for the objective judgement, it was compared to existing methods, and the PSNR(peak signal to nise ratio) was set as the judgement standard.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2017.05a
• /
• pp.343-345
• /
• 2017

In digital era image processing has been utilized in a variety of media such as TV, camera and smart phone. However, in the process of analyzing, recognizing, and processing image data, deterioration occurs due to various causes and Salt & Pepper noise occurs. Typical methods for removing such noise include SMF, CWMF, and SWMF. However, existing methods have a somewhat poor noise canceling characteristic in Salt & Pepper noise environment. Therefore, in this paper, we propose an algorithm to remove Salt & Pepper noise effectively by using bilinear interpolation method and median filter according to noise density of local mask. And using the PSNR(Peak Signal to Noise Ratio) it compared to the existing methods and their performance in order to determine the performance of the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.30 no.6C
• /
• pp.465-474
• /
• 2005

In this paper, a directional reconstruction of lost block in image over noisy channel is presented. DCT coefficients or pixel values in the lost blocks are recovered by using the linear interpolation with available neighboring blocks that are adaptively selected by the directional measure that are composed of the DDC (Difference of DC opposite blocks)and SAC(Similarity of AC opposite blocks) between opposite blocks around lost blocks. The proposed directional recovery method is effective for the strong edge and texture regions because we do not make use of the fixed 4-neighboring blocks but exploit the varying neighboring blocks adaptively by the directional information in the local image. In this paper, we describe the novel directional measure(CDS: Combination of DDC and SAC) composed of the DDC and the SAC and select the usable block to recover the lost block with the directional measure. The proposed method shows about 0.6dB PSNR improvement in average compared to the conventional methods.

• Journal of Digital Contents Society
• /
• v.15 no.6
• /
• pp.737-743
• /
• 2014

For single image super resolution (SR), interpolation based and example based algorithms are extensively used. The interpolation algorithms have the strength of theoretical simplicity. However, those algorithms are tending to produce high resolution images with jagged edges, because they are not able to use more priori information. Example based algorithms have been studied in the past few years. For example based SR, the nearest neighbor based algorithms are extensively considered. Among them, neighbor embedding (NE) has been inspired by manifold learning method, particularly locally linear embedding. However, the sizes of local training sets are always too small. So, NE algorithm is weak in the performance of the visuality and quantitative measure by the poor generalization of nearest neighbor estimation. An improved NE algorithm with Support Vector Regression (SVR) was proposed to solve this problem. Given a low resolution image, the pixel values in its high resolution version are estimated by the improved NE. Comparing with bicubic and NE, the improvements of 1.25 dB and 2.33 dB are achieved in PSNR. Experimental results show that proposed method is quantitatively and visually more effective than prior works using bicubic interpolation and NE.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.11
• /
• pp.2029-2036
• /
• 2017

Currently, the demand for multimedia services is increasing with the rapid development of the digital age. Image data is corrupted by various noises and typical noise is mainly AWGN, salt and pepper noise and the complex noise that these two noises are mixed. Therefore, in this paper, the noise is processed by classifying AWGN and salt and pepper noise through noise judgment. In the case of AWGN, the outputs of spatial weighted filter and pixel change weighted filter are composed and processed, and the composite weights are applied differently according to the standard deviation of the local mask. In the case of salt and pepper noise, cubic spline interpolation and local histogram weighted filters are composed and processed. This study suggested the multiple image restoration filter algorithm which is processed by applying different composite weights according to the salt and pepper noise density of the local mask.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.12
• /
• pp.2946-2952
• /
• 2014

Among the Example based Super Resolution(SR) techniques, Neighbor embedding(NE) has been inspired by manifold learning method, particularly locally linear embedding. However, the poor generalization of NE decreases the performance of such algorithm. The sizes of local training sets are always too small to improve the performance of NE. We propose the Learning Sparse-Neighbor Image Representation baesd on SVR having an excellent generalization ability to solve this problem. Given a low resolution image, we first use bicubic interpolation to synthesize its high resolution version. We extract the patches from this synthesized image and determine whether each patch corresponds to regions with high or low spatial frequencies. After the weight of each patch is obtained by our method, we used to learn separate SVR models. Finally, we update the pixel values using the previously learned SVRs. Through experimental results, we quantitatively and qualitatively confirm the improved results of the proposed algorithm when comparing with conventional interpolation methods and NE.

• The Journal of the Acoustical Society of Korea
• /
• v.41 no.2
• /
• pp.122-129
• /
• 2022

In this paper, we propose a method to improve the performance of Transformer-based speech recognizers using data augmentation that locally adjusts the frame rate. First, the start time and length of the part to be augmented in the original voice data are randomly selected. Then, the frame rate of the selected part is changed to a new frame rate by using linear interpolation. Experimental results using the Wall Street Journal and LibriSpeech speech databases showed that the convergence time took longer than the baseline, but the recognition accuracy was improved in most cases. In order to further improve the performance, various parameters such as the length and the speed of the selected parts were optimized. The proposed method was shown to achieve relative performance improvement of 11.8 % and 14.9 % compared with the baseline in the Wall Street Journal and LibriSpeech speech databases, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.6
• /
• pp.1215-1220
• /
• 2016

As society has developed rapidly toward a highly advanced digital information age, a multimedia communication service for acquisition, transmission and storage of image data as well as voice has being commercialized. However, image data is always corrupted by various noises during image processing, so researches for removing noises have been continued until now. There are diverse types of noise on the image including salt and pepper noise, AWGN, and mixed noise. Hence, the filter algorithm for the image recovery was proposed that salt and pepper noise was processed by linear interpolation, histogram weighted values and median filter after defining the noise to lessen the impact of mixed noise added in the image, and AWGN was processed by the pixel information of local mask establishing the weighted values in this study. In addition, the algorithm was compared with the conventional methods for objectively and used the PSNR(peak signal to noise ratio) as the basis of the determination.