Image denoising using a nonlinear pixel-likeness weighted-frame technique

Abstract

Recent advances in the development of image denoising applications for eliminating the various sources of noise in digital images have employed hard-ware platforms based on field programmable gate arrays for attaining speed and efficiency, which are essential factors in real-time applications. However, image denoising providing for maximum denoising performance, speed, and efficiency on these platforms is subject to constant innovation. To this end, the present work proposes a high-throughput fixed-point adaptive edge noise filter architecture to denoise digital images with additive white Gaussian noise in realtime using a non-linear modified pixel-likeness weighted-frame technique. The proposed architecture works in two stages. The first stage involves normal and conditional sorting. The second stage is a decision-oriented output selection unit. Decision-oriented adaptive windowing is included for better impulse noise suppression and edge preservation. The denoising performance of the proposed denoising scheme is demonstrated to be superior to those currently available state-of-the-art approaches. Moreover, the power consumption is reduced by 25.01% compared to conventional algorithms.