Effective and Fast Estimation for Image Sensor Noise Via Constrained Weighted Least Squares

Area of Science:

• Image processing
• Computational imaging
• Signal processing

Background:

• Traditional noise models assume signal independence, which is inaccurate for real image sensor data.
• Signal-dependent noise is a critical factor in raw image processing and denoising.
• Accurate noise estimation is essential for effective image enhancement algorithms.

Purpose of the Study:

• To develop an effective and fast method for estimating image sensor noise from a single raw image.
• To model noise as a signal-dependent process, which is more representative of real-world image data.
• To improve the performance of image denoising and other image processing tasks by providing accurate noise parameters.

Main Methods:

• Proposed a novel method for estimating noise model parameters using constrained weighted least squares (WLS) fitting.
• Developed a fast patch selection scheme guided by image histograms to identify weakly textured regions.
• Incorporated a sample credibility measure based on texture strength to enhance fitting robustness.

Main Results:

• The proposed method achieves state-of-the-art noise estimation performance.
• Experimental results show significantly faster execution times compared to existing noise estimation schemes.
• The method effectively handles signal-dependent noise in raw image sensor data.

Conclusions:

• The developed method provides an efficient and accurate approach for image sensor noise estimation.
• This technique is particularly valuable for processing raw images where noise is signal-dependent.
• The findings contribute to advancing image denoising and other image processing applications.