Deconvolution in Deep Learning, its importance, and where to use it?

Both Convolution and Deconvolution are used for image processing. The goal of the convolution layer is to transform an image into an abstract representation of output values. The convolution algorithm downsizes the image that means if the size of the input image is 6 x 6 then the image will be downsized to a 4 x 4 or 3 x 3. Deconvolution is a mathematical operation that reverses the process of the convolution layer. Now throw the output through the deconvolutional layer, and you get back the same input. The importance of Deconvolution is to regenerating the image from the convolution output layer, by doing this we can be sure that the convolution is extracting the correct features from the image.