| Summary: | 碩士 === 國立交通大學 === 電機與控制工程系所 === 93 === There are two main parts in this thesis. The first part of the system is a color cast detector using the neural network. In this stage, the test images can be classified as having no cast, real cast, or intrinsic cast (image presenting a cast due to a predominant color that must be preserved). We have a data set of 700 images downloaded from internet, or acquired using various digital cameras. Choose 350 images from the data set as the training images for the neural network, and the rest 350 images for testing. From each training image, we can acquire 13 statistical parameters, and let them as the input vectors of the neural network. We also provide the neural network with the corresponding target vectors, and the supervised training method is used to train the neural network. The second stage is the white balance algorithm. If the real cast is found by color cast detector, the white balance algorithm should be applied on the test image. The test image is divided into n blocks. For each block, the output weighting can be obtained by a fuzzy system and the luminance weighted value is also calculated. Finally, we can obtain the new amplifier gains of the R, G and B channel to remove the color cast.
The difficulty of the cast detector is how to distinguish the intrinsic cast from the test images. Existing methods of cast detection are threshold method [12] and histogram method [13], the performance of neural network cast detector will compared with these two methods. The proposed white balance algorithm will compared with the gray world assumption [1], max white method [2], and standard deviation weighted gray world assumption [21] by experimenting on the Macbeth color charts with color cast. Some representative images which contents are nature scenes will be tested by the proposed method. The experimental results show that the performance of detecting the intrinsic cast inside the test images using neural network cast detector is better than the histogram method. For the images which have less number of colors or small standard deviation of colors, the proposed white balance algorithm can improve the quality of color correction.
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