In this paper, a new wavelet threshold denoising algorithm has been proposed based on the correlation
characteristics between layers coefficient and the inner-layer coefficient. For each wavelet coefficient,
a corresponding threshold is constructed according to the wavelet coefficients between layers and
layer-related features. The experimental results show that the ability of this algorithm is better than the
traditional algorithm in the aspect of image denoising.
The differences between the wavelet denoising method and traditional denoising method have been analyzed in this
paper. Wavelet coefficients of the neighborhood characteristics have been analyzed on the basis of original global
threshold value. A new algorithm based on the characteristics of the neighborhood has been proposed by establishing
windows for each coefficient. It is found that the new algorithm is more effective than traditional algorithms at the aspect
of improving the quality of the denoised images.
In this paper the wavelet transform and the wavelet transform theory have been elaborated. A wavelet coefficients'
model in which individual shrinkage thresholds are selected for each coefficient has been established. The relationships
between different wavelet coefficients are analyzed. According to this new model, a new algorithm of image denosing
has been proposed. It is shown in the experiments that the new algorithm is more effective than traditional algorithms.
Several wavelet denoising methods based on wavelet transform, such as modulus maximum and threshold denoising,
have been proposed in this paper. In modulus maximum method, we use a new piecewise cubic spline interpolating
(PCSI) algorithm instead of alternate projection (AP) method to reconstruct wavelet coefficients. Middle threshold is
presented based on wavelet shrinkage. It overcomes not only the discontinuous of hard-threshold algorithm but also the
shortcoming that there is an invariable dispersion between the estimated wavelet coefficients and decomposed wavelet
coefficients of soft-threshold algorithm. At the same time, it is more elastic than the soft- threshold and hard-threshold
Edge detection is one of the most basic contents in image processing and identification and plays an important role in the
image processing. A new edge detection method based on the multi-scale of wavelet analysis and improving the image
segmentation of the best threshold is proposed. The characteristics of this new edge detection operator have been
analyzed in this paper. The advantages and disadvantages between the new operator and those traditional edge detection
operators have also been discussed.
This work introduces a new algorithm for image smoothing. Nonlinear partial differential equations (PDEs) are
employed to smooth the image while preserving the edges and corners. Compared with other filters such as average filter
and median filter, it is found that the effects of image denoising by the new algorithm are better than that by other filters.
The experimental results show that this method can not only remove the noise but also preserve the edges and corners.
Due to its simplicity and efficiency, the algorithm becomes extremely attractive.
The visual document image is the electronic image about newspapers, books or magazines taken by the digital camera, the digital vidicon etc. Whose getting is more convenient than got from the scanner. Along with the development of OCR technology, visual document images could be recognized by OCR. Affected by some factors, digital image will be degraded during its acquisition, processing, transmission. One of the main problems affecting image quality, leading to unpleasant pictures, comes from improper exposure to light. So preprocessing is becoming much more significant before recognition in order to get an appropriate image satisfied recognition requirements. For the low contrast images with underexposure, according to the visual document image's characteristic, a new algorithm, based on image background separation, for image object enhance is proposed, The proposed method calculate the threshold of separation firstly, And different processing be taken on foreground and background: Various gray values in image background will be merged into unitary gray value, whereas the contrast of foreground will be enhanced. The proposed algorithm implemented in Visual C++ 6.0, and compared the result of proposed algorithm with the results of Otsu's method and histogram equalization. The experimental results show clearly that this algorithm could enhance the details of image object adequately, increase the recognition rate, and avoid the block effect at the same time.