Automatic Illumination and Color Compensation Using Mean Shift and Sigma Filter Heechul Han and Kwanghoon Sohn IEEE Transactions on Consumer Electronics, Vol. 55, No. 3, AUGUST 2009 Reporter: Chun-Jung Chen

Outline Introduction Edge-preserving smoothing filter ICCMS Experimental results Conclusion

Introduction(1/4) Photography can be expressed as "drawing with light" because it is about capturing light and recording it. However, it is difficult for amateur photographers to control light perfectly under arbitrary lighting conditions.

Introduction(2/4) The human visual system (HVS) can easily construct a visual representation with vivid color and detail across a wide range of lighting variations through a series of adaptive mechanisms for brightness perception and processing. In contrast, digital cameras do not have such sophisticated features similar to HVS.

Introduction(3/4) Consequently, the visual quality of captured images suffers from a loss of detail, especially in low-light and shadow regions, and color shifts in photos taken at sunset or incandescent light.

Introduction(4/4)

Edge-preserving smoothing filter(1/2)

Edge-preserving smoothing filter(2/2)

ICCMS Image statistics analysis Alpha blending based on human perception Layer decomposition Illumination suppression Illumination compensation Color restoration function

Image statistics analysis For automatic processing, the parameter is adaptively obtained by image statistics analysis. First of all, mosaic image can be produced by image “mosaicing” transformation for the fast operation as follows: We confirm that standard score is good method for the parameter estimation given by

Alpha blending based on human perception(1/2) We apply alpha blending between the compensated image and the input image for estimating proper brightness and color enhancement. The ratio of alpha blending between input image and compensated image is given by

Alpha blending based on human perception(2/2) After the compensated image is transformed back to RGB color space using a simple matrix transformation, the final image can be computed as followed:

Layer decomposition Our detail layer has edge suppression term based on HVS for attenuating considerable amounts of noise in dark regions in an image.

Illumination suppression(1/2)

Illumination suppression(2/2)

Illumination compensation We use the base layer that is the edge-preserving low-pass filtered image of the luminance channel through a mean shift filter and a sigma filter for the illumination estimation, which can be expressed using the following equation:

Color restoration function The proposed color restoration function(CRF) is defined as follows:

Experimental results(1/4)

Experimental results(2/4)

Experimental results(3/4)

Experimental results(4/4)

Conclusion ICCMS is satisfied with appropriate illumination compensation, vivid color restoration, artifacts suppression, automatic parameter estimation. Plan to implement in the image signal processing chip for the digital camera.

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