"Efficient cascading of multi-domain image Gaussian noise filters"
Journal of Real-Time Image Processing; accepted: March 2019

Meisam Rakhshanfar and Maria Amer
Contact: amer att ece.concordia.ca

Image denoising is a well explored but still an active research topic. The focus is usually on achieving higher numerical quality which is theoretically interesting, however, often the factor of computation cost is not considered. Our idea is to employ different image Gaussian noise filters to construct an effective image denoiser, where the deficiency of each filter is compensated with others, while a wide variation of quality versus speed can be achieved. We integrate filters using different cascaded forms and show that if two filters use uncorrelated features, their cascaded form provides a higher quality than each separately. We start with easy-to-implement filters employing pixel- and frequency-domain with different kernel size to construct a fast yet high-quality multi-domain denoiser. Then, we propose more complex denoisers by integrating our cascaded multi-domain denoiser to other state-of-the-art denoising methods. Simulations show that the quality of proposed multi-domain denoiser is significantly higher than its building-blocks. We also show that the proposed multi-domain denoiser can be integrated to state-of-the-art denoisers to from a more effective denoiser, while adding negligible complexity.

Software Releases

• CMDF image denoiser Matlab!
  Download CMDF image denoiser software and the demo files for Matlab.
• CMDF image denoiser Python! (Linux, Mac, Windows)
  Download CMDF image denoiser software and the demo files for Python
• Cascading demo Matlab!
  Download the demo files for testing cascaded denoisers for NLB and BM3D
• Denoising test images Dataset!
  Download CMDF image denoiser software and the demo files for Python