This paper presents local area enhancement of the segmented color image obtained from the multi-spectral image clustering by using FCM (fuzzy c-means). In case, the multi-spectral images, which have the number of bands more than that of 3, must decrease the data volume to remain the number of bands of 3 in order to correspond with the meaning of red, green, and blue images. PCA (Principal Components Analysis) is then used to transform original multi-spectral images into PCA images. The first three components having information more than that of original images of 95% is assigned as red, green and blue images, namely RGB color image. FCM clustering apply to RGB color image, separately. This method is called the PCA-FCM technique being the multi-spectral image clustering. By applying such technique, the result images consisted of red, green, and blue images separately are the segmented images. By histogram equalization algorithm, the result of local area enhancement based on a number of clusters as the segmented image can solve effect of intensity saturation from global area enhancement and the perceptibility of color image is clearly improved.
Since RGB images derived from multispectral (TM) images will lose some information, in this paper we present the method to solve such a problem by using principal component analysis (PCA) which transforms TM images into the principal component images (PCs), while the high resolution PAN data is decomposed by wavelet transform. Thus, RGB images are assigned by the first three principal component images which normally have approximately 95% of the information in the original images. The intensity image from RGB to HIS transformation is replaced by the lower frequency coefficient of wavelet transform of PAN data corresponding to multispectral images. HIS to RGB transformation is then applied. The fused RGB image using our method can obtain more details.
In this paper we propose a subband image compression by using wavelet transform to split original images. Each of subband images is then quantized by an adaptive vector quantization with dynamic bit allocation based on advantage of nature of wavelet coefficients. The energy of each subband image, except the lowest frequency subband image will not be quantized, will be sorted from minimum to maximum. Energy of each subband image is calculated to allocate bits not over the desired bit rate. The accumulation of energy from these subband images will be divided into 4 groups. First two lower energy groups will be encoded with 256 and 1 6 code vectors for 1 6 pixels block size in accordance with energy ratio. Others will be encoded with 256 code vectors for 4 and 16 pixels block size. Based on the given bit rate, the total dynamical bit rate of each group is calculated. If the total dynamical bit rate in the group is less or more than the given bit, it will thenbe adjusted based on the energy of subband image in only the same group. The remaining of energy from higher energy group will be carried to lower. The experiments are shown that the resulting images from the proposed method can be clearly improved by Peak Signal to Noise Ratio (PSNR) of 36.30 16, MSE =15.2377, 1 .03 125 bpps.
It is common that most document forms opt for the use of straight line as a reference position for filled information. The automated data-entry systems of such documents require an ability to search these reference lines so that the location of information in the forms can be known. This paper proposes a wavelet-based algorithm for extracting these reference lines in business forms. Stationary wavelet transform is used to transform a gray-level document image into different frequency-band images. The horizontal detail subband is then selected and passed through a post-processing to produce a binary bitmap of reference lines. The experimental results on synthetic and real document images will be given to illustrate the usefulness of such an algorithm.