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Image Signal Decomposition and Reconstruction Based on Wavelets
... Jingjing et al. [6] proposed that if a proper selection of wavelet and a desired level of decomposition is chosen, it will effectively provide a better quality of an image. Debashis Das [7] summarized that multi-scale works on the image features at a scale level. ...
The objectivity of illumination and reflectance has been the continuous thrust area in image analysis and enhancement. To achieve better quality, the researchers have focused on improving contrast enhancement and brightness preservation of mainly non- uniform images. This paper reviews the computational techniques evolved for improving the visual quality of images. The discussion has been focused on multi-scale decomposition approach to achieve the desired variation in contrast and brightness of the images. The multi-scale approach is termed as a combination of coarse and detail coefficients of the input image. So, it may be said that the detail characteristics are evaluated to study directional variations of the intensity in the images.
The main purpose of this paper is to introduce the construction of orthogonal, biorthogonal and semi-orthogonal multiwavelet and the application of multiwavelet to signal processing. In application, because of the particularity of multiwavelet it's necessary to deal with the sampling data of original signal, namely, the preprocessing procedure. Preprocessing procedure is the important step in application of the multiwavelet. It's relative to the choice of the multiwavelet basis, There has the corresponding preprocessing method based on the different multiwavelet basis.
This paper introduces some applications in image processing with wavelets, such as edge detection, pattern recognition and image segmentation. Edge detection includes contour extraction and background removal for an illustration of gray - level, extraction of tumor region in color image and enlargement of type size of Chinese character. As for recognition of Chinese character or character at first, we can reduce the dimensionality of two dimensional patterns to one dimensional sign, then do wavelets decomposition for these one dimensional patterns, at last compute fractal dimension of each sub - pattern, so feature vectors can be obtained and used to recognition. The characters after affine transformation can also be recognized by a dyadic wavelet affine invariant function. At last we introduce two means of image segmentation. One is based on energy analysis, another is singularity analysis.
For the imaging characteristics of multi-focus images, multi-spectral images and panchromatic images, since the Shearlet transform has better properties to sparse express the characteristics of the images, a kind of new image fusion rules is proposed. Moreover, based on the fusion rules, the algorithm of adaptive fusion rules based on Shearlet transform is proposed. In the algorithm of multi-focus images fusion, the different focus images are transformed with Shearlet transform respectively, and the decomposed low-frequency coefficients and high-frequency coefficients were fused according to the proposed fusion rules. It is verified that the proposed algorithm has better clarity and richer details information compared with many algorithms. Multi-spectral and panchromatic images fusion algorithm is proposed based on combination of Shearlet and HSV transform. Firstly, the multi image is transformed with HSV transform; then, the gotten V component is Shearlet transformed with pan image and the specific fusion rules is chosen for the decomposition coefficient in the fusion process; finally, the new V and H, S components are transformed with inverse HSV transform. This algorithm reached a good balance in the two aspects of spatial resolution and spectral characteristics. The fused images can reduce spectral distortion, and effectively enhance the spatial resolution. The simulation experiments show that the proposed algorithm has better fusion performance and visual effect, compared to traditional multi-spectral and panchromatic images fusion algorithms.
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