Image Restoration with Signal-dependent Camera Noise
This work addresses image quality issues for photography and imaging applications, but it is incremental as it modifies existing algorithms rather than introducing a new paradigm.
The authors tackled image restoration under signal-dependent camera noise by developing a fast iterative algorithm for denoising and deconvolution, adapting traditional Gaussian noise-based methods to handle mixed Poisson-Gaussian noise and quantization errors.
This article describes a fast iterative algorithm for image denoising and deconvolution with signal-dependent observation noise. We use an optimization strategy based on variable splitting that adapts traditional Gaussian noise-based restoration algorithms to account for the observed image being corrupted by mixed Poisson-Gaussian noise and quantization errors.