No abstract

Noise removal (also called as denoising) of an image is a vital task in multi-class image classification. Three major shortcomings in Weighted Nuclear Norm Minimization (WNNM) are identified. Firstly, WNNM's patch matching based on the noisy data will considerably augment the risk of patch mismatching. This shortcoming is overcome by performing the grouping task based on noise contentment. Secondly, the fixed feedback percentage which keeps on feeds back ten percent of the residual image to the next iteration despite the consequences of noise levels. This shortcoming is ruled out by incorporating relative feedback mechanism. Finally, the unchanged / constant number of iterations for different noise not considering the distinctions in image content that which will certainly fails to deem the degree of detail in the image. For this variable termination criterion is used. The proposed work is named as Trio Constrained Adaptive Noise Removal (TCANR). Performance metric peak signal to noise ratio (PSNR) is chosen. Four existing methods are taken into account for comparing the proposed TCANR. Extensive simulations are conducted using MATLAB and the results prove that the proposed TCANR performs better in terms of PSNR when compared with the existing methods.

Many dimensions of research work are carried out in the digital image processing. Multi-label image classification is one such thrust research paradigm in digital image processing. Edge detection plays a vital role in image segmentation for multi-label image classification. In our previous research paper noise removal is carried out. In this research work an improved Gaussian filter technique that makes use of automatic anisotropic factor is added up with the Gaussian filter is used. The automatic anisotropic factor aims to decrease the time taken for edge detection. Implementations are carried out using MATLAB. 40 images from four different datasets are used in the implementation. Time taken for edge detection is the performance metric taken. Simulation results prove that the improved Gaussian filter technique using automatic anisotropic factor decreases the time taken for edge detection.