The use of the homomorphic filter technique is described in order to enhance the contrast in the mammographic images, which is adopted to the dyadic wavelet transform. The proposed method has employed the nonlinear enhancement in homomorphic filtering as well as denoising method in the wavelet domains. Experimental results show that the homomorphic filtering method improves the contrast in breast tumor images such that the contrast improvement index is increased by two fold compared to the conventional wavelet-based enhancement technique.

The blind separation of sources is a recent and important problem in signal processing. Since 1984, it has been studied by many authors whilst many algorithms have been proposed. In this paper, the description of the problem, its assumptions, its currently applications and some algorithms and ideas are discussed.

A 14. 4-in. diagonal EL display with 640128 pixels has been developed in red/green multicolor structures by using a new phosphor layer consisting of Zn1-xMgxS:Mn and ZnS:Mn. The display is designed for 240 Hz-frame rate, enabling the luminance to be improved by a factor of two. In addition, the contrast ratio is strongly enhanced by optimizing the black background structure and color filters. Improved characteristics make it possible for the EL panel to meet the requirements for the public information display taking advantages of high-reliability, crisp image and wide-viewing angle. Furthermore, the possibility of full-color EL displays will be described on the basis of "color by white" approach.

A two-cascaded image processing approach to enhance the subtle differences in X-ray CT image is proposed. In the method, an asymmetrical non-linear subfilter is introduced to reduce the noise inherent in the image while preserving local edges and directional structural information. Then, a subfilter is used to compress the global dynamic range of the image and emphasize the details in the homogeneous regions by performing a modular transformation on local image den-sities. The modular transformation is based on a dynamically defined contrast fator and the histogram distributions of the image. The local contrast factor is described in accordance with Weber's fraction by a two-layer neighborhood system where the relative variances of the medians for eight directions are computed. This method is suitable for low contrast images with wide dynamic ranges. Experiments on X-ray CT images of the head show the validity of the method.