Performance Analysis of Spatial and Transform Filters for Efficient Image Noise Reduction
This addresses noise reduction in digital images for communication applications, but it is incremental as it primarily reviews and compares existing methods.
The paper tackled image denoising by comparing existing algorithms like filtering and wavelets with bilateral filters, showing that an efficient filtering approach yields better performance in terms of metrics such as Peak Signal to Noise Ratio, Mean Square Error, and Universal Quality Identifier.
During the acquisition of an image from its source, noise always becomes an integral part of it. Various algorithms have been used in past to denoise the images. Image denoising still has scope for improvement. Visual information transmitted in the form of digital images has become a considerable method of communication in the modern age, but the image obtained after the transmission is often corrupted due to noise. In this paper, we review the existing denoising algorithms such as filtering approach and wavelets based approach and then perform their comparative study with bilateral filters. We use different noise models to describe additive and multiplicative noise in an image. Based on the samples of degraded pixel neighbourhoods as inputs, the output of an efficient filtering approach has shown a better image denoising performance. This yields promising qualitative and quantitative results of the degraded noisy images in terms of Peak Signal to Noise Ratio, Mean Square Error and Universal Quality Identifier.