Multiclass Noisy Image Classification Based on Optimal Threshold and Neighboring Window Denoising

Classification of multi class images is very enviable for different recognition. This is affected by many factors such as noise, blur, low illumination, complex background, occlusion etc. Noise is one of the major factors causing degradation of the classification performance. This paper propose an efficient method for classification of multi class object images which are corrupted by Gaussian noise based on wavelet feature extraction. This work shows robustness of proposed method over the spatial domain denoising and feature extraction for classification.

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