Amalgamation of ROAD-TGM and progressive PCA using performance booster method for detail persevering image denoising

This paper presents a two-stage sequential method for noisy grayscale and color images. At first proposed method enhances the accuracy of the noise detection stage by using spatial domain filter rank-order absolute difference trimmed global mean (ROAD-TGM) along with transform domain-based progressive principle component analysis (PPCA) method. Then the performance booster algorithm is used to ensure the proximity of restored value to the original value. Quite often in real-world applications images are corrupted with Salt & Pepper noise, strip lines artifact and Blotches artifact. We observed the proposed method is capable of removing the above-mentioned noises and artifacts with comparatively better accuracy. The proposed method uses the progressive PCA for its dimension reduction ability and local information of image restored by ROAD-TGM to provide enhanced noise detection performance. Before noise removal, a performance booster algorithm eliminates noisy values by using sequential hard thresholding and estimates the tentative original values automatically. Then algorithm decides the suitable value for the restoration of noise pixel by using a structural similarity index (SSIM) to ensure the proximity of the restored image to the original image. The proposed algorithm is tested on a standard set of color and grayscale images to ensure the versatility of the proposed algorithm. The experiment shows that the proposed algorithm achieves high denoising performance for noise and artifact while maintaining the visually important details.