High Dynamic Range (HDR) images have been widely applied in daily applications. However, HDR image is a special format, which needs to be pre-processed known as tone mapping operators for display. Since the visual quality of HDR images is very sensitive to luminance value variations, conventional watermarking methods for low dynamic range (LDR) images are not suitable and may even cause catastrophic visible distortion. Currently, few methods for HDR image watermarking are proposed. In this paper, two watermarking schemes targeting HDR images are proposed, which are based on µ-Law and bilateral filtering, respectively. Both of the subjective and objective qualities of watermarked images are greatly improved by the two methods. What's more, these proposed methods also show higher robustness against tone mapping operations.

In this paper, we propose a coding algorithm for High Dynamic Range Images (HDRI). Our encoder applies a tone mapping model based on scaled µ-Law encoding, followed by a conventional Low Dynamic Range Image (LDRI) encoder. The tone mapping model is designed to minimize the difference between the tone-mapped HDRI and its LDR version. By virtue of the nature of the µ-Law model, not only the quality of the HDRI but also the one of the LDRI is improved, compared with a state of the art in conventional HDRI coding methods. Furthermore the error limit caused by our encoding is theoretically analyzed.

Adverse weather, such as rain or snow, can cause difficulties in the processing of video streams. Because the appearance of raindrops can affect the performance of human tracking and reduce the efficiency of video compression, the detection and removal of rain is a challenging problem in outdoor surveillance systems. In this paper, we propose a new algorithm for rain detection and removal based on both spatial and wavelet domain features. Our system involves fewer frames during detection and removal, and is robust to moving objects in the rain. Experimental results demonstrate that the proposed algorithm outperforms existing approaches in terms of subjective and objective quality.