Akbar Sahil Ali ; Verma Ananya

Abstract

Noise, an unwanted component in an image, can be the reason for thedegradation of Image at the time of transmission or capturing. Noise reductionfrom images is still a challenging task. Digital Image Processing is acomponent of Digital signal processing. A wide variety of algorithms can beused in image processing to apply to an image or an input dataset and obtainimportant outcomes. In image processing research, removing noise from imagesbefore further analysis is essential. Post-noise removal of images improvesclarity, enabling better interpretation and analysis across medical imaging,satellite imagery, and radar applications. While numerous algorithms exist,each comes with its own assumptions, strengths, and limitations. The paper aimsto evaluate the effectiveness of different filtering techniques on images witheight types of noise. It evaluates methodologies like Wiener, Median, Gaussian,Mean, Low pass, High pass, Laplacian and bilateral filtering, using theperformance metric Peak signal to noise ratio. It shows us the impact ofdifferent filters on noise models by applying a variety of filters to variouskinds of noise. Additionally, it also assists us in determining which filteringstrategy is most appropriate for a certain noise model based on thecircumstances.