| Summary: | 碩士 === 國立交通大學 === 電子工程系 === 89 === Color image interpolation finds application in image capture and image rescaling and as such, it is an important topic in multimedia signal processing. Mathematical morphology possesses simple but effective operators and is a well-known tool in image processing. The basic operations such as dilation, erosion, opening and closing retain the structuring features of an image such as shape. On the other hand, fuzzy logic has been popular in machine control and other industrial applications. Recently, it has also been considered for image processing such as noise removal, texture classification and edge detection. We consider their use in color image interpolation. In particular, we consider the problems of color image zoom-in and CCD/CMOS sensor color recovery.
Firstly, we consider color image interpolation by simple morphological opening operation. Base on the properties of our target applications, we develop the structuring element for this purpose. For image zoom-in application, we see that the interpolation results are reasonable for most of the experimented image patterns. However, there is a little enlargement effect for small, high-intensity areas, which is undesirable. For CCD/CMOS sensor color recovery, it produces false colors.
Secondly, we consider edge-classified morphological opening interpolation. We use a morphological gradient operation to classify the images into sharp and smooth areas according to the morphological gradients. After it, we use a morphological opening operator in the sharp areas and the bilinear interpolator in the smooth areas. We see that the false colors are reduced in CCD/CMOS sensor color recovery. For the enlargement effect in image zoom-in, it produces a little improvement.
Finally, we consider edge-classified fuzzy interpolation, in which, after classifying the pixels in an image, we use fuzzy interpolation in sharp areas. We see that it reduces the enlargement effect and false colors.
The performance of the image processing is mainly judged from human subjectivity. For reference, we list the mean square errors of the different interpolation methods. We see that the morphological opening interpolator has the most mean square errors in most patterns. That is because, opening interpolator has less error points, but the error values at the error points are larger.
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