Analytical Comparison of Noise Reduction Filters for Image Restoration Using SNR Estimation
This work addresses image quality improvement for applications like medical imaging or photography, but it is incremental as it focuses on analytical comparison of existing filters.
The paper tackled the problem of noise removal in image restoration by comparing noise reduction filters with similar properties, using Signal-to-Noise-Ratio (SNR) estimation to evaluate their effects on noisy images.
Noise removal from images is a part of image restoration in which we try to reconstruct or recover an image that has been degraded by using apriori knowledge of the degradation phenomenon. Noises present in images can be of various types with their characteristic Probability Distribution Functions (PDF). Noise removal techniques depend on the kind of noise present in the image rather than on the image itself. This paper explores the effects of applying noise reduction filters having similar properties on noisy images with emphasis on Signal-to-Noise-Ratio (SNR) value estimation for comparing the results.