Design of two-dimensional recursive filters by using neural networks

Area of Science:

• Digital Signal Processing
• Machine Learning Applications
• Filter Design

Background:

• Two-dimensional (2-D) recursive digital filters are essential in image and signal processing.
• Existing design methods for these filters can be complex and computationally intensive.
• Developing efficient and accurate filter design techniques remains an active research area.

Purpose of the Study:

• To introduce a new design methodology for 2-D recursive digital filters.
• To leverage neural networks for solving the filter design problem.
• To demonstrate the efficacy and advantages of the proposed method.

Main Methods:

• The core of the method involves formulating the 2-D filter design as a constrained minimization problem.
• A suitable neural network is employed to find the solution to this minimization problem through convergence.
• The proposed technique is validated using a numerical example.

Main Results:

• The neural network-based method successfully designed the 2-D recursive digital filter.
• Performance comparison with existing methods on the same numerical example was conducted.
• The proposed method demonstrated advantages over previously published techniques.

Conclusions:

• The presented neural network-based design method offers a viable and potentially superior alternative for 2-D recursive digital filters.
• The approach simplifies the design process by converting it into a solvable optimization problem.
• Further research can explore the scalability and applicability of this method to more complex filter structures.