When it comes to mobile phone autofocus using contrast detection, also known as contrast focus, this particular focusing method involves highly complex mathematical derivations. As someone who isn't particularly strong in math but has a basic understanding of engineering principles, I can offer only a rough overview. For a more detailed explanation, those with greater expertise should weigh in.
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In general, there are two primary approaches to autofocus based on image processing. The first method determines the depth of focus, while the second calculates the depth of defocus. Explaining the principles behind these methods can be quite intricate, but in essence, the depth of defocus approach derives blur and depth information from out-of-focus images and uses this along with relevant shooting parameters to compute a sharpness evaluation value. Based on these values, it determines how much adjustment is needed. Since fewer images are required, this method tends to be faster.
On the other hand, the depth of focus method is more involved. It requires a sequence of images with varying degrees of blurriness to calculate the sharpness evaluation values for each. From these values, an evaluation curve is derived through fitting, and the optimal focus position is identified based on the peak of this curve. This method offers higher precision but at the cost of speed.
It’s worth noting that despite similar processes, different manufacturers employ slightly different calculation methods. Consequently, the convergence speed of contrast focus can vary significantly between brands.
One common aspect of these methods is the sharpness evaluation value, which functions somewhat like a statistic derived from specific parameters. Naturally, this value must meet certain criteria, including effectiveness and robustness.
There are several ways to calculate sharpness evaluation values, though this list is not exhaustive:
Firstly, there's the spectral function method. A clear image contrasts sharply with a blurry one, containing richer spectral components. By evaluating the amplitude of the spectral function, we can assess sharpness.
Secondly, information entropy is another approach. A clear image typically has a higher information entropy compared to a blurry one. While proving this rigorously can be cumbersome, the data processing theorem simplifies things: 1) Clear images can be processed into blurry ones, but blurry images cannot be made clear. Hence, the information entropy of a clear image is greater than that of a blurry one. If we have a way to compute entropy, it can serve as a basis for evaluation.
Thirdly, the gradient function measures the rate of change of a variable within an image. A correctly focused image exhibits sharper edges, where the edges often exhibit the maximum rate of change or the least continuous change. Thus, we can evaluate sharpness this way.
Currently, the most widely used method is the gradient. Mathematically, the gradient represents the rate of change. Practically, we can calculate this using variance, energy gradients, Laplacians (equivalent to second-order derivatives), or other methods. Each method has its own computational complexity, but the choice of pixels in the image is a critical consideration. For instance, does contrast focus select a central point, four edge points, or five corner points? This decision is crucial for every camera manufacturer.
Let’s consider now: which of the following is the evaluation method?
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