ENHANCED NOISE CANCELLATION IMAGE PERFORMANCE USING WEIGHTED AVERAGE (WAV) REPROJECTION

Main Objective: This research targets AP sizing entrenched on image structure to raise denoising performance using an improved method for classifying image pixels. Background problem: Digital images may be blended by noise while the addition or communication process, affecting the authentic image signal. Image noise can cause problems at several stages of image processing equally image distribution. Accordingly, image denoising is a significant activity to recover the initial clean image signal from the detected noise signal. Novelty: The proposed WAV method has been refined and improved regarding the classification scheme, and the APS, and the classification results can be used as a mask on the noise image to fix identical patches. Research Method: This study proposes a WAV reprojection algorithm, with the PS being set dynamically entrenched on the image structure. Image structures are consistently taken with an upgraded and enhanced analysis method entrenched in the structure tensor matrix. Analysis results are also used to develop the analysis of comparable patches in images. Finding/Result: Empirical outcomes present that the noise cancellation work of the suggested method is better than the authentic WAVRA, along with several other modifications of the NLMA. Conclusion: In intensity profiles, the proposed method mostly has fewer changes to the original image values than other methods, thus, this method can be continued again to color images, and can also be applied to various types of data such as medical images.
 
Keywords: Weighted Average (WAV), Noise Cancellation Image, Non-Local Means (NLM), Adaptive Patch