Proposed Algorithm Overview
Abstract
Color correction is an essential image processing operation that transforms a camera-dependent RGB color space to a standard color space, e.g., the XYZ or the sRGB color space. The color correction is typically performed by multiplying the camera RGB values by a color correction matrix, which often amplifies image noise. In this paper, we propose an effective color correction pipeline for a noisy image. The proposed pipeline consists of two parts; the color correction and denoising. In the color correction part, we utilize spatially varying color correction (SVCC) that adaptively calculates the color correction matrices for each local image block considering the noise effect. Although the SVCC can effectively suppress the noise amplification, the noise is still included in the color corrected image, where the noise levels spatially vary for each local block. In the denoising part, we propose an effective denoising framework for the color corrected image with spatially varying noise levels. Experimental results demonstrate that the proposed color correction pipeline outperforms existing algorithms for various noise levels.
Reference
- Effective Color Correction Pipeline for A Noisy Image
Kenta Takahashi, Yusuke Monno, Masayuki Tanaka and Masatoshi Okutomi
IEEE International Conference on Image Processing (ICIP2016), pp.4002-4006, September, 2016. [PDF, Poster]
*Note for correction: We noticed that the CPSNR results of LNCC and SVCC are reversely reported in Table 1 of the original ICIP paper. We corrected this error and apologize for any inconveniences this may have caused.
Demo Code
MATLAB code, version 1.0, August 25, 2016
Results
Numerical Comparisons
Visual Comparisons
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