Analyzing Noise Models and Advanced Filtering Algorithms for Image Enhancement
This work addresses noise reduction in images for applications like medical imaging and satellite imagery, but it is incremental as it compares existing methods without introducing new algorithms.
The paper evaluated eight filtering techniques (Wiener, Median, Gaussian, Mean, Low pass, High pass, Laplacian, and bilateral) on images with eight types of noise using Peak Signal-to-Noise Ratio (PSNR) as a metric, showing their impact and helping determine the most appropriate filter for specific noise models.
Noise, an unwanted component in an image, can be the reason for the degradation of Image at the time of transmission or capturing. Noise reduction from images is still a challenging task. Digital Image Processing is a component of Digital signal processing. A wide variety of algorithms can be used in image processing to apply to an image or an input dataset and obtain important outcomes. In image processing research, removing noise from images before further analysis is essential. Post-noise removal of images improves clarity, 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 aims to evaluate the effectiveness of different filtering techniques on images with eight types of noise. It evaluates methodologies like Wiener, Median, Gaussian, Mean, Low pass, High pass, Laplacian and bilateral filtering, using the performance metric Peak signal to noise ratio. It shows us the impact of different filters on noise models by applying a variety of filters to various kinds of noise. Additionally, it also assists us in determining which filtering strategy is most appropriate for a certain noise model based on the circumstances.