Question About Linear and Circular Convolution - 1D and 2D
Let me answer you:


For a signal of size $ m $ and a filter of size $ n $ the output of Linear Convolution is $ n + m - 1 $. In case of 2D signal of size $ \left( m, n \right) $ and filter of size $ \left( p, q \right) $ the output size is $ \left( m + p - 1, n + q - 1 \right) $.
You can read about Circular Convolution in Wikipedia. Basically when a convolution is applied on finite discrete signals one should take care of the boundaries. In most cases the default is assuming the signal i padded with zeros which results in Linear Convolution. If you use padding which build a periodic / circular signal and then apply convolution you will get Circular Convolution. It turns out that frequency domain multiplication of discrete signals is equivelnt of Circular Convolution in spatial domain.
You need to pad it with zeros and line the axis origin to match the image.
Have a look at my answer for Kernel Convolution in Frequency Domain - Cyclic Padding. I also shared a MALAB code which shows how to build the kernel correctly.