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Published on Nov 30, 2023


Many image processing operations such as scaling and rotation require re-sampling or convolution filtering for each pixel in the image. Convolutions on digital images are important since they represent operations that are more general than the operations that can be performed on analog images.

Convolution has many applications which have great significance in discrete signal processing. It is usually difficult to deal with analog signals. Hence signals are converted to digital state.

Filtering of signals is very important in order to determine which one to accept and which one to reject, and all of that is done by convolution.

This paper presents a direct method of reducing convolution processing time using hardware computing and implementations of discrete linear convolution of two finite length sequences (NXN).

This implementation method is realized by simplifying the convolution building blocks. The purpose of this research is to prove the feasibility of an FPGA that performs a convolution on an acquired image in real time.

The proposed implementation uses a modified hierarchical design approach, which efficiently and accurately speeds up computation; reduces power, hardware resources, and area significantly.

The efficiency of the proposed convolution circuit is tested by embedding it in a top level FPGA. In addition, the presented circuit uses less power consumption and delay from input to output. It also provides the necessary modularity, expandability, and regularity to form different convolutions for any number of bits




Simulation: modelsim5.8c

Synthesis: Xilinx 9.1