Does not using more filters in deeper CNN creates more images?
No, your understanding is not correct.
Each of the 64 filters of the second layer will be applied to each of the 32 channels from the output of the first layer, resulting in 64 channels in the output of the second layer.
When the input of a convolutional layer has multiple channels, the convolution filter itself has the same number of channels. In your example, if we are using $3\times3$ filters, each filter in the second layer will be a tensor of dimensions $3\times3\times32$. Therefore, the filter &quot;covers&quot; the full depth of the input. Then, you simply perform the element-wise multiplication of the filter with the overlapping region in the input and add all the resulting elements together. Applying just 1 filter, we obtain a result with 1 channel.
This way, the number of channels of the output of a convolutional layer is the same as the number of filters in the convolution.