Color Image and Video Enhancement by Emre Celebi, Michela Lecca, Bogdan Smolka

By Emre Celebi, Michela Lecca, Bogdan Smolka

This textual content covers state of the art colour picture and video enhancement concepts. The e-book examines the multivariate nature of colour image/video facts because it relates to distinction enhancement, colour correction (equalization, harmonization, normalization, balancing, fidelity, etc.), noise elimination and smoothing. This booklet additionally discusses colour and distinction enhancement in imaginative and prescient sensors and functions of snapshot and video enhancement.

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1 R. Zhen and R. L. Stevenson Joint Demosaicing and Deblurring One of the common artifacts in digital images is the blur which may be caused by the non-ideal response of the camera sensor, the out-of-focus capturing, and the object or camera motion. The blurred image is usually modeled as the convolution of a sharp color image and a blur kernel (also called point spread function, PSF) plus noise. Most existing deblurring algorithms use the result that has gone through demosaicing and other post-processing steps as the blurred observation, but applying deblurring after demosaicing is suboptimal.

34] observed that filtering a CFA image could better preserve high frequencies than filtering each color component separately. The Fourier transform of the CFA image F CFA (u, v) can be easily calculated using Eq. 50) s t [FR − 2FG + FB ](u − , v − ) 2 2 L(u, v) is the luminance component at the center of the spectrum, while C1 (u, v) and C2 (u, v) are chrominance components shifted to the sides and corners respectively. 15 shows the energy distribution in the CFA spectrum. Since the color difference components C1 and C2 only contain low frequencies, they have a more compact spectrum that has less overlap with the luminance spectrum, as compared with the overlap between spectrum replicas in the subsampled components.

It can be noticed that the ratio and difference images carry out less high-frequency information than the green plane. Moreover, in areas where red is saturated, the ratio image contains more high-frequency information than the difference image, which makes the interpolation result more artifact-prone [35]. The spatial correlation reflects the fact that within a homogeneous image region, neighboring pixels share similar color values [10]. One could use this principle to estimate the missing color components at any pixel location except the pixels near the edge since these pixels have neighbors which do not belong to the same homogeneous region.

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Categories: Computer Vision Pattern Recognition