Median Filtering for Removal of Maximum Impulse Noise from Images with a Decision Based Model

Abstract

Filtering the unwanted parameters will guarantee the quality of images for further operations. Sensors and other devices used to capture the images may be subjected to situations where it might not function as programmed. The medium in which the images are transformed may also face the same difficulties. In such cases, salt and pepper noise should be removed before the images are taken into considerations. Otherwise, processes like edge detection, image segmentation and object recognition will be facing hurdles and may not reveal the desired output. This proposal intends to solve such an issue by using an algorithm for median filtering for using filters with medians on highly affected images. The previous techniques used for filtering out noise are Standard Median Filtering (SMF), Adaptive Median Filtering (AMF) and they have shown relatively lesser performance than the proposed approach. Implementation includes a FPGA set up and strives to remove impulse noise to a great extent consuming lesser computation time. The quality of output's visual and quantitative metrics has outperformed the discussed previous models.

4th Order Shannon Energy Envelope Approach for Localization of S1 and S2 for Early-Stage Detection of Heart Valve Dysfunction

“…In certain signals, a few undesired time-lobes were get appeared or some desired time-lobes were get suppressed, leading to false detection. To endure this situation, a peak correction algorithm, based on modified median filtering was incorporated [19].…”

“…If any 9 th time-lobe appears, then that would be supposed as an unwanted peak. Therefore, for removal of the unwanted peaks i.e., false time-lobe-peaks; a peak correction algorithm was incorporated which was based on a modified median filtering concept [19,20]. It improved the performance of the process to a greater extent, as shown in Figure 10.…”