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
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