Automatic white balance plays a key role in digital color imaging system based on CCD/CMOS sensors. The gray world
method and its variants are widely used for their simplicity. However, they will fail if the image is dominated by only
one or two colors. The iterative method, which extracts gray color points from the image for color temperature
estimation, performs well if there are enough gray color points. But it does not work in the case of serious color casts or
lack of gray color points. Thus, a new method is proposed combining the iterative method and the gray world method.
The iterative method is for fine adjustment, while the gray world method is for coarse adjustment. The characteristics of
the digital microscope are taken into account as well. There are three major contributions in the paper. First, brightness
constraint is considered during the gray color points detection. The detecting procedure is more precise as a result.
Second, each frame of the video stream is divided into n-by-n blocks so as to increase the immunity to the noise. Last,
the fine adjustment and the coarse adjustment are combined together. The 'Fine-Coarse-Fine' routine adjusts the image
properly even though there are not sufficient gray color points. Experiments on digital microscope indicate that the
proposed automatic white balance method is robust, effective and efficient.
With the limitation of the depth of focus in microscope, especially in high-power objectives, different areas in the field of view have different focal planes and therefore only sharp focused images in a small part of the view can be obtained. A three-dimension micro-image fusion algorithm is introduced based on wavelet transform (WT) in microscope auto-focusing system. The step motor is driven continuously moving across the vicinity of the focal plane to obtain the micro image information of the relevant position. With real-time multi-focused images acquired and saved, the image fusion algorithm is applied to finally get a complete and sharp three-dimension image. This can be used to extend the depth of focus in microscope and in three-dimension reconstruction in biologic slice, rock specimen, and so on. The particularity brought by the image fusion of a real-time system is mainly studied as well as the algorithm based on this condition. Here we emphasize the multi-focus fusion algorithm based on WT. Images acquired from the digital camera are transformed from spatial domain to wavelet domain. All the details from different images are extracted and processed from the wavelet coefficients and then are recombined to form the new coefficient of the resulting image. By taking an inverse WT, we finally get the final three-dimension image. This system has two main advantages: real-time processing and accurate fusion result.
A microscope auto-focusing system using the self-adaptive mountain-climbing search (SAMCS) method is introduced
based on personal computer (PC) control. It mainly consists of four parts: the microscope, the digital camera to get the
video images, the mechanical part of step motor and the computer to control the focusing process. The precision of the
auto-focusing system is to some extent improved through high-resolution color images acquired by the digital camera as
well as high subdivision of the step motor drive. An improved searching method--the SAMCS method is presented here.
It can effectively improve the searching efficiency while guaranteeing the precision of the auto-focusing system. Based
on the normal mountain-climbing search (MCS) algorithm, the SAMCS method takes full consideration of omnidistance
concept and local extreme point influences. Thereby it can adaptively adjust the searching range according to different
environmental conditions, and has quite good robustness. This feature mainly has two advantages. First, this method is
much more exact than the normal mountain-climbing, which can not avoid local fluctuations. Second, it is much faster
than the method of only using omnidistance searching to avoid local fluctuations. At the same time, we also take
evaluation function and region selection into consideration to reach a faster and more accurate focusing result. And the
experimental result demonstrates a good efficiency and accuracy.