SVM Ensemble-based Noise Detection Method for Image Denoising

Abstract

Background: To preserve sharp edges and image details while removing noise, this paper presents a denoising method based on Support Vector Machine (SVM) ensemble for detecting noise. Methods: The proposed method ISVM can be divided into two stages: noise detection and noise recovery. In the first stage, local binary features and weighted difference features are extracted as input features vector of ISVM, and multiple sub-SVM classifiers are integrated to form the noise classification model of ISVM by iteratively updating the sample weight. The pixels are divided into noise points and signal points. In the noise recovery stage, according to the classification results of the previous stage, only the gray value of the noise point is replaced, and the replacement value is the weighted mean value with the reciprocal of the quadratic square of the distance as the weight. Results: Finally, the replacement value at the noise point and the original pixel value of the signal point are reconstructed to get the denoised image. Conclusion: The experiments demonstrate that ISVM can achieve a noise detection rate of up to 99.68%. ISVM is highly effective in the denoising task, produces a visually pleasing denoised image with clear edge information, and offers remarkable improvement compared to that of the BPDF and DAMF.