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Hua Lu
Hunan University of International Economics
Computer Science & IT

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Image Denoising Based on K-means Singular Value Decomposition Jian Ren; Hua Lu; Xiliang Zeng
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 4: December 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i4.1897

Abstract

The image is usually polluted by noises in its acquisition and transmission and noises are of great importance in the image quality, therefore, image de-noising has become a significant technique in image analysis and processing. In the image de-noising based on sparse representation, one of the hot spots in recent years, the useful image information has certain structural features, which coincide with the atomic structure while noises don’t have such features, therefore, sparse representation can separate the useful information from the noises effectively so as to achieve the purpose of de-noising. In view of the above-mentioned theoretical basis, this paper proposes an image de-noising algorithm of sparse representation based on K-means Singular Value Decomposition (K-SVD). This method can integrate the construction and optimization of over-complete dictionary, train the atom dictionary with the image samples to be decomposed and effectively build the atom dictionary that reflects various image features to enhance the de-noising performance of the algorithm in this paper. Through simulation analysis, this method can conduct noise filtration on the image with different noise densities and its de-noising effect is also better than other methods.
Image Restoration Based on Hybrid Ant Colony Algorithm Yan Feng; Hua Lu; Xiliang Zeng
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 4: December 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i4.1900

Abstract

Image restoration is the process to eliminate or reduce the image quality degradation in the digital image formation, transmission and recording and its purpose is to process the observed degraded image to make the restored result approximate the un-degraded original image. This paper, based on the basic ant colony algorithm and integrating with the genetic algorithm, proposes an image restoration processing method based on hybrid ant colony algorithm. This method transforms the optimal population information of genetic algorithm into the original pheromone concentration matrix of ant colony algorithm and uses it to compute the parameters of degradation function so as to get a precise estimate of the original image. By analyzing and comparing the restoration results, the method of this paper can not only overcome the influence of noises, but it can also make the image smoother with no fringe effects in the edges and excellent visual effects, verifying its practicability.
A Fractal Image Compression Method Based on Multi-Wavelet Yan Feng; Hua Lu; XiLiang Zeng
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 3: September 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i3.1809

Abstract

How to effectively store and transmit such multi-media files as image and video has become a research hotspot. The traditional compression algorithms have a relatively low compression ratio and bad quality of decoded image, at present, the fractal image compression method with a higher compression ratio fails to meet the requirements of the practical applications in the quality of the compressed image as well as the coding and decoding time. This paper integrates fractal thought and multi-wavelet transform and proposes a fractal image compression algorithm based on multi-wavelet transform. To transform the image model into a combination of relevant elements in the frequency domain instead of merely building on the foundation of the neighborhood gray-scale correlation has the ability to code larger image blocks, eliminates the possibility of global correlation in the image and improves the coding speed of the existing fractal image compression algorithm. The experimental result shows that the algorithm proposed in this paper can accelerate the coding speed of the present fractal image compression and have certain self-adaptivity while slightly reducing the quality of decoding image.
Co-Authors Jian Ren Xiliang Zeng Yan Feng