在上一节中,我们讨论了小波的概念,如缩放和移动。现在我们来看两种类型的小波变换:连续小波变换和离散小波变换。连续小波分析的主要应用有:时频分析和时域频率分量的滤波。离散小波分析的主要应用是信号和图像的去噪和压缩。正如我在前一节中提到的,这两种转换的区别在于它们如何离散尺度和转换参数。我们将讨论这些技术在1-D场景中的应用。让我们仔细看看连续小波变换(CWT)。您可以使用此转换来获得信号的同时时频分析。分析小波最适合于时频分析,因为这些小波没有负的频率成分。这个列表包含了一些适合连续小波分析的分析小波。 The output of CWT are coefficients, which are a function of scale or frequency and time. Let’s now discuss the process of constructing different wavelet scales. Recall from our previous video that, when you scale a wavelet by a factor of 2, it results in reducing the equivalent frequency by an octave. With the CWT, you have the added flexibility to analyze the signal at intermediary scales within each octave. This allows for fine scale analysis. This parameter is referred as the number of scales per octave (Nv). The higher the number of scales per octave, the finer the scale discretization. Typical values for this parameter are 10, 12, 16, and 32. The scales are multiplied with the sampling interval of the signal to obtain a physical significance. Here is an example of scales for a bump wavelet with 32 scales per octave. The signal is sampled every 7 micro seconds. This is the corresponding plot with the equivalent frequency for the scales. Notice that the actual scale values are exponential. Now, each scaled wavelet is shifted in time along the entire length of the signal and compared with the original signal. You can repeat this process for all the scales, resulting in coefficients that are a function of the wavelet’s scale and shift parameter. To put it in perspective, a signal with 1000 samples analyzed with 20 scales results in 20,000 coefficients. In this way, you can better characterize oscillatory behavior in signals with the Continuous wavelet transform. The discrete wavelet transform or DWT is ideal for denoising and compressing signals and images, as it helps represent many naturally occurring signals and images with fewer coefficients. This enables a sparser representation. The base scale in DWT is set to 2. You can obtain different scales by raising this base scale to integer values represented in this way. The translation occurs at integer multiples represented in this equation. This process is often referred to as a dyadic scaling and shifting. This kind of sampling eliminates redundancy in coefficients. The output of the transform yields the same number of coefficients as the length of the input signal. Therefore, it requires less memory. The discrete wavelet transform process is equivalent to comparing a signal with discrete multirate filter banks. Conceptually, here is how it works: Given a signal - S, - the signal is first filtered with special lowpass and high pass filter to yield lowpass and highpass sub-bands. We can - refer to these as A1 and D1. Half of the samples are discarded after filtering as per the Nyquist criterion. The filters typically have a small number of coefficients and result in good computational performance. These filters also have the ability to reconstruct the sub bands, while cancelling any aliasing that occurs due to downsampling. For the next level of decomposition, the lowpass subband (A1) is iteratively filtered by the same technique to yield narrower subbands - A2 and D2 and so on. The length of the coefficients in each sub band is half of the number of coefficients in the preceding stage. With this technique, you can capture the signal of interest with a few large magnitude DWT coefficients, while the noise in the signal results in smaller DWT coefficients. This way, the DWT helps analyze signals at progressively narrower subbands at different resolutions. It also helps denoise and compress signals.
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