Reverse biorthogonal wavelets are highly regular wavelets with compact support and symmetric filters and they have explicit construction. This paper explores the performance of the reverse biorthogonal spline wavelets in denoising images differentiated by the detail-contents in the images. The transform used in the study is the Undecimated Wavelet Transform which is a translation-invariant transform. The selected images are corrupted by adding white Gaussian noise to produce noisy test images. The study shows that the denoising effect depends on the amount of details in the image. It is also seen that reverse biorthogonal spline wavelets are highly effective in denoising dense-detail images like fingerprints. These wavelets also give good denoising for low-detail images like human face. The best wavelet in the family for each of these purposes has been sorted out. Rbio 3.1 is found to be an odd member of the family. These wavelets are found to give poor results in denoising medium-detail images. The study finds application in Forensic science and in restoration of facial images and when the images encountered in such applications contain several types of noise distributions simultaneously.

Reverse biorthogonal, Spline, Undecimated Transform, Image detail

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