Wyniki wyszukiwania - BazTech

1

A combinatorial based technique for impulsive noise removal in images

Rital S., Bretto A., Cherifi H., Aboutajdine D.

Image Processing & Communications

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2001

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Vol. 7, no 3-4

115-129

EN

The aim of this work is the elimination of the impulsive noise from an image using hypergraph theory. We introduce an image model called Adaptive Image Neighborhood Hypergraph (AINH). From this model we propose a combinatorial definition of noisy data. A detection procedure is used to classify hyperedges either as noisy or clean data. Similar to other techniques, the proposed algorithm uses an estimation procedure to remove the effects of noise. The efficiency of the proposed method was tested on gray scale images using objective image quality measures. The results show that the new method outperforms standard impulsive noise reduction algorithms.

2

A classified vector quantization scheme for color image coding perceptually tuned

Charrier C., Cherifi H.

Image Processing & Communications

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2001

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Vol. 7, no 1-2

17-29

EN

Edges are of fundamental importance in the analysis of images, and of course in the field of image quality. To incorporate the edgy: information as coded by the Human Visual System (HVS) in a vector quantization scheme, we have developed a classification strategy to separate edge vectors from non-edge vectors. This strategy allows the generation of different sets of codewords different size for each kind of vectors. For each one of the "edge" sets, the final size is perceptually tuned. Finally, when an image is encoded, its associated edge map is generated. Then the selection of the appropriate "edge" set is made in respect with the edge amount present in the image. Then the second set of non-edge vectors is performed in order to respect the required compression rate. Statistical measure and psychophysical experiments have been performed to judge the quality of reconstructed images.

3

Lossless optimization of fractal image coding

El Haziti M., Aboutajdine D., Cherifi H.

Machine Graphics and Vision

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2000

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Vol. 9, No. 1/2

201-206

EN

In farcal image compression the large amount of computations needed for the encoding stage is a major obstacle that needs to be overcome. Several methods have been developed for reducing the computational complexity. They can be divided into lossy and lossless approaches. In this paper we introduce a lossless approach where the search space is reduced to the admissible solutions space. For still images, experiments shown that this method, greatly improves pairing search as compared to the exhaustive search. In can be easily integrated in most lossy acceleration schemes.