Adaptive Reconfigurable Architecture for Image Denoising

Abstract

In this paper, we propose an adaptive reconfigurable architecture for image denoising. First part of this paper outlines an efficient noise detection hardware for Gaussian & impulse noise detection and suitable filters for denoising. With a robust noise detection method including a novel Gaussian noise detection method, we also explore the dynamic detection of noise in an image giving adaptability to the architecture for a better quality of denoising. Proposed architecture includes a decision making unit to find out the presence of noise as well as type of the noise, based on which a suitable filter is employed during run-time. An onboard microprocessor controls the reconfiguration and dataflow. Proposed architecture is tested on Xilinx Virtex-6 FPGA with localized noise and mixed noise conditions and it gives superior performance compared to the standard filters used. High quality denoising is achieved with simple filters on a reconfigurable region utilizing smaller area and lesser hardware resources. � 2015 IEEE.