Digital Image Processing

Sachin Pohar ¹, K. Ashok Babu ¹, G. Kesavan Pillai ²

Affiliations
1 Sri Indu College of Engg., Hyd, AP, IN
2 Pricol Limited, Hyd, AP, IN

Abstract

This paper proposes a high capacity reversible image watermarking scheme based on integer to integer wavelet transforms. The proposed scheme divides an input image into non-overlapping blocks and embeds a watermark into the high-frequency wavelet coefficients of each block. The conditions to avoid both underflow and overflow in the spatial domain are embedded includes not only messages but also side information used to reconstruct the exact original image. To minimize the mean squared distortion between the original and the watermarked images given a payload, the watermark is adaptively embedded into the image. The experimental results show that the proposed scheme achieves higher embedding capacity while maintaining distortion at a lower level than the existing reversible watermarking schemes.

Keywords

Reversible Watermarking, Wavelet Transform, Watermark Embedding and Extraction.

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K. Ashok Babu ¹, G. Kesavan Pillai ², Nalla Nandakishore ¹

Affiliations
1 Sri Indu College of Engg., Hyd, AP, IN
2 National Remote Sensing Agency, Hyd, AP, IN

Abstract

Single sensor digital color still/video cameras capture images using a color filter array (CFA) and require color interpolation (demosaicking) to reconstruct full color images. The color reproduction has to combat sensor noises which are channel dependent. If untreated in demosaicking, sensor noises can cause color artifacts that are hard to remove later by a separate Denoising process, because the demosaicking process complicates the noise characteristics by blending noises of different color channels. This paper presents a joint demosaicking-denoising approach to overcome this difficulty. The color image is restored from noisy mosaic data in two steps. First, the difference signals of color channels are estimated by linear minimum mean square-error estimation. This process exploits both spectral and spatial correlations to simultaneously suppress sensor noise and interpolation error. With the estimated difference signals, the full resolution green channel is recovered. The second step involves in a wavelet-based Denoising process to remove the CFA channel-dependent noises from the reconstructed green channel. The red and blue channels are subsequently recovered. Simulated and real CFA mosaic data are used to evaluate the performance of the proposed joint demosaicking-denoising scheme and compare it with many recently developed sophisticated demosaicking and denoising schemes.

Keywords

Bayer Pattern, Color Demosaicking, Color Filter Array (CFA), Denoising, Wavelet.

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