Many conventional image processing algorithms such as noise filtering, sharpening and deblurring, assume a noise model of Additive White Gaussian Noise (AWGN) with constant standard deviation throughout the image. However, this noise model does not hold for images captured from typical imaging devices such as digital cameras, scanners and camera-phones. The raw data from the image sensor goes through several image processing steps such as demosaicing, color correction, gamma correction and JPEG compression, and thus, the noise characteristics in the final JPEG image deviates significantly from the widely-used AWGN noise model. Thus, when the image processing algorithms are applied to the digital photographs, they may not provide optimal image quality after the image processing due to the inaccurate noise model. In this paper, we propose a noise model that better fits the images captured from typical imaging devices and describe a simple method to extract necessary parameters directly from the images without any prior knowledge of imaging pipeline algorithms implemented in the imaging devices. We show experimental results of the noise parameters extracted from the raw and processed digital images.

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The user experience of printing web pages has not been very good. Web pages typically contain contents that are not printworthy or informative such as side bars, footers, headers, advertisements, and auxiliary information for further browsing. Since the inclusion of such contents degrades the web printing experience, we have developed a tool that first selects the main part of the web page automatically and then allows users to make adjustments. In this paper, we describe the algorithm for selecting the main content automatically during the first pass. The web page is first segmented into several coherent areas or blocks using our web page segmentation method that clusters content based on the affinity values between basic elements. The relative importance values for the segmented blocks are computed using various features and the main content is extracted based on the constraint of one DOM (Document Object Model) sub-tree and high important scores. We evaluated our algorithm on 65 web pages and computed the accuracy based on area of overlap between the ground truth and the extracted result of the algorithm.

brightness invariance, illumination invariant, feature descriptors, image features, feature matching, correspondencesWe describe a novel and robust feature descriptor called ordinal spatial intensity distribution (OSID) which is invariant to any monotonically increasing brightness changes. Many traditional features are invariant to intensity shift or affine brightness changes but cannot handle more complex nonlinear brightness changes, which often occur due to the nonlinear camera response, variations in capture device parameters, temporal changes in the illumination, and viewpoint-dependent illumination and shadowing. A configuration of spatial patch sub-divisions is defined, and the descriptor is obtained by computing a 2-D histogram in the intensity ordering and spatial sub-division spaces. Extensive experiments show that the proposed descriptor significantly outperforms many state-of-the-art descriptors such as SIFT, GLOH, and PCA-SIFT under complex brightness changes. Moreover, the experiments demonstrate the proposed descriptor's superior performance even in the presence of image blur, viewpoint changes, and JPEG compression. The proposed descriptor has far reaching implications for many applications in computer vision.

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