Denoising images is a most difficult task in applications for image processing. The image specifics are preserved and the additional sounds found in the images are removed. It is also a challenge to remove noise from medical and satellite images. It improves the diagnostic capacity of medical images and satellite images visual clarity. The noise in the images varies and its density varies depending on imaging techniques. The algorithms in the literature were suggested based on the noise density and the forms of noise. The aim of this paper is to eliminate the noise from ultrasound, magnetic resonance images and satellite images using an effective denoisation algorithm Hybrid Wiener Adaptive Weighted Median filter (HWAWMF) which is the combination of Wiener and Adaptive Centre Pixel Weighted Median Filter (ACPWMEF). In terms of performance parameters with an improved Peak to Signal Noise Ratio(PSNR), the hybrid filter shows better results than ACPWMEF. The Vienna filter takes out the additional noises in the images thus blurs the image’s optical perception. And also uses optimization approaches to enhance the image consistency. This paper proposes HWAWMF (PSO HWAWMF) based on particle swarm optimization and HWAWMF based on dragonfly optimization algorithms (DOAF HWAWMF). Visual vision and PSNR also have been improved by using the optimising algorithm at an average of 3.18 db, 4.83 db, and 3.14 db for lower noise (0.0% to 30%), medium noise (40% to 60%) as well as high noise density (70% to 90%). The efficacy of the algorithm using MATLAB R2013 is verified through both medical images, simulated and actual. In order to assess the computer complexity of the Altera algorithm for location, power and time using Cyclone II EP2C35F672C6, Cyclone II and Stratix III EP3SL150F1152C2, this algorithm is also implemented in the Altera Field Programable Gate Array (FPGA).