Performance prediction for 3D filtering of multichannel images

Oleksii Rubel (1) , Ruslan A. Kozhemiakin (1) , Sergey K. Abramov (1) , Vladimir V. Lukin (1) , Benoit Vozel (2) , Kacem Chehdi (2)

1 Kharkov National University
2 IETR - Institut d'Électronique et des Technologies du numéRique

Performance of denoising based on discrete cosine transform applied to multichannel remote sensing images corrupted by additive white Gaussian noise is analyzed. Images obtained by satellite Earth Observing-1 (EO-1) mission using hyperspectral imager instrument (Hyperion) that have high input SNR are taken as test images. Denoising performance is characterized by improvement of PSNR. For hard-thresholding 3D DCT-based denoising, simple statistics (probabilities to be less than a certain threshold) are used to predict denoising efficiency using curves fitted into scatterplots. It is shown that the obtained curves (approximations) provide prediction of denoising efficiency with high accuracy. Analysis is carried out for different numbers of channels processed jointly. Universality of prediction for different number of channels is proven

Domains: Engineering Sciences [physics]; Electronics; Computer Science [cs]; Networking and Internet Architecture [cs.NI]

Oleksii Rubel, Ruslan A. Kozhemiakin, Sergey K. Abramov, Vladimir V. Lukin, Benoit Vozel, et al.. Performance prediction for 3D filtering of multichannel images. Image and Signal Processing for Remote Sensing XXI, 96430D (October 15, 2015), 2015, Toulouse, France. pp.96430D--96430D--11, ⟨10.1117/12.2193976⟩. ⟨hal-01300961⟩