Estimation of signal-dependent sensor noise via sparse representation of noise level functions

This paper proposes a noise estimation method for signal-dependent sensor noise based on sparse representation of noise level functions (NLFs). Homogeneous blocks are detected by an image structure analyzer, and grouped to estimate noise levels for various image intensities with confidences. The noise level function is recovered from the incomplete and noisy estimated samples by solving its sparse representation under a trained basis. Experimental results show that our proposed method accurately estimates NLFs for both smooth and highly-textured images over various noise levels.