# Adaptive Non-linear Filtering Technique for Image Restoration Source: [Adaptive Non-linear Filtering Technique for Image Restoration]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:08:48.414Z Authors: S. K. Satpathy, S. Panda, K. K. Nagwanshi, S. K. Nayak, C. Ardil Published: 2022-04-20T08:36:59.000Z ## Abstract Removing noise from the any processed images is very important. Noise should be removed in such a way that important information of image should be preserved. A decisionbased nonlinear algorithm for elimination of band lines, drop lines, mark, band lost and impulses in images is presented in this paper. The algorithm performs two simultaneous operations, namely, detection of corrupted pixels and evaluation of new pixels for replacing the corrupted pixels. Removal of these artifacts is achieved without damaging edges and details. However, the restricted window size renders median operation less effective whenever noise is excessive in that case the proposed algorithm automatically switches to mean filtering. The performance of the algorithm is analyzed in terms of Mean Square Error \[MSE\], Peak-Signal-to-Noise Ratio \[PSNR\], Signal-to-Noise Ratio Improved \[SNRI\], Percentage Of Noise Attenuated \[PONA\], and Percentage Of Spoiled Pixels \[POSP\]. This is compared with standard algorithms already in use and improved performance of the proposed algorithm is presented. The advantage of the proposed algorithm is that a single algorithm can replace several independent algorithms which are required for removal of different artifacts. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()