Guang Deng

A Generalized Unsharp Masking Algorithm

Enhancement of contrast and sharpness of an image is required in many applications. Unsharp masking is a classical tool for sharpness enhancement. We propose a generalized unsharp masking algorithm using the exploratory data model as a unified framework. The proposed algorithm is designed to address three issues: 1) simultaneously enhancing contrast and sharpness by means of individual treatment of the model component and the residual, 2) reducing the halo effect by means of an edge-preserving filter, and 3) solving the out-of-range problem by means of log-ratio and tangent operations. We also present a study of the properties of the log-ratio operations and reveal a new connection between the Bregman divergence and the generalized linear systems. This connection not only provides a novel insight into the geometrical property of such systems, but also opens a new pathway for system development. We present a new system called the tangent system which is based upon a specific Bregman divergence. Experimental results, which are comparable to recently published results, show that the proposed algorithm is able to significantly improve the contrast and sharpness of an image. In the proposed algorithm, the user can adjust the two parameters controlling the contrast and sharpness to produce the desired results. This makes the proposed algorithm practically useful.

An adaptive Gaussian filter for noise reduction and edge detection

Gaussian filtering has been intensively studied in image processing and computer vision. Using a Gaussian filter for noise suppression, the noise is smoothed out, at the same time the signal is also distorted. The use of a Gaussian filter as pre-processing for edge detection will also give rise to edge position displacement, edges vanishing, and phantom edges. Here, the authors first review various techniques for these problems. They then propose an adaptive Gaussian filtering algorithm in which the filter variance is adapted to both the noise characteristics and the local variance of the signal.<<ETX>>

The study of logarithmic image processing model and its application to image enhancement

Describes a new implementation of Lees (1980) image enhancement algorithm. This approach, based on the logarithmic image processing (LIP) model, can simultaneously enhance the overall contrast and the sharpness of an image. A normalized complement transform has been proposed to simplify the analysis and the implementation of the LIP model-based algorithms. This new implementation has been compared with histogram equalization and Lees original algorithm.

Differentiation-Based Edge DetectionUsing the Logarithmic Image Processing Model

The logarithmic image processing (LIP) model is a mathematical framework which provides a specific set of algebraic and functional operations for the processing and analysis of intensity images valued in a bounded range. The LIP model has been proved to be physically justified by that it is consistent with the multiplicative transmittance and reflectance image formation models, and with some important laws and characteristics of human brightness perception. This article addresses the edge detection problem using the LIP-model based differentiation. First, the LIP model is introduced, in particular, for the gray tones and gray tone functions, which represent intensity values and intensity images, respectively. Then, an extension of these LIP model notions, respectively called gray tone vectors and gray tone vector functions, is studied. Third, the LIP-model based differential operators are presented, focusing on their distinctive properties for image processing. Emphasis is also placed on highlighting the main characteristics of the LIP-model based differentiation. Next, the LIP-Sobel based edge detection technique is studied and applied to edge detection, showing its robustness in locally small changes in scene illumination conditions and its performance in the presence of noise. Its theoretical and practical advantages over several well-known edge detection techniques, such as the techniques of Sobel, Canny, Johnson and Wallis, are shown through a general discussion and illustrated by simulation results on different real images. Finally, a discussion on the role of the LIP-model based differentiation in the current context of edge detection is presented.

An Entropy Interpretation of the Logarithmic Image Processing Model With Application to Contrast Enhancement

The logarithmic image processing (LIP) model is a mathematical theory that provides new operations for image processing. The contrast definition has been shown to be consistent with some important physical laws and characteristics of human visual system. In this paper, we establish an information-theoretic interpretation of the contrast definition. We show that it can be expressed as a combination of the relative entropy and Shannons information content. Based on this new interpretation, we propose an adaptive algorithm for enhancing the contrast and sharpness of noisy images.

A signal denoising algorithm based on overcomplete wavelet representations and Gaussian models

In this paper, we propose a simple signal estimation algorithm based on multiple wavelet representations and Gaussian observation models. The proposed algorithm has two major steps: a joint-optimum estimation of the wavelet coefficients and an averaging of the denoised images. Experimental results show that the denoising performance of proposed algorithm is comparable to that of the state of the art.

A Fast Watermarking System for H.264/AVC Video

In this paper, we propose a fast watermarking system that works on the H.264/AVC motion vectors. By restricting access to DCT coefficients and pixel information, the computational complexity of the watermark embedder/extractor is kept low and much lower than that of the H.264 decoder. The error propagation due to motion prediction compensation is monitored and its effect is limited by a tracking method that is based solely on the motion information from the bitstream. Although this work focuses on the H.264/AVC standard, the novel watermarking technique is also applicable to the MPEG1-2 and MPEG4 video standards

Real-Time Vision-Based Stop Sign Detection System on FPGA

Many fatal accidents have happened due to drivers failing to stop at stop signs. A stop sign recognition system could be used to reduce the risk of accidents by warning the driver when a vehicle approaches a stop sign at an unexpected speed. In this paper, we describe the implementation of a real-time vision-based stop sign recognition system on a Xilinx Virtex-4 Field Programmable Gate Array (FPGA) device. This system uses a variant of the Histogram of Oriented Gradient (HoG) feature set and the efficient integral map for processing. Simulation and in-vehicle test results show good potential for deploying the system in practice. The FPGA system is able to process 60 frames of 752times480 pixels per second.

Adaptive linear prediction for lossless coding of greyscale images

We present a new prediction method for lossless image coding. In this prediction scheme, the prediction for each pixel is formed by adaptively combining the predicted values from a set of least squares based linear predictors. The combination scheme follows the main idea of the Bayesian model averaging to reduce prediction error due to model uncertainty. A lossless image coding algorithm based on this prediction method is also presented. Experimental results show that the compression performance of this algorithm is better than that of the TMW 0.51. It is also close to that of a newly improved version of TMW.

A general framework for the second-level adaptive prediction

We present a study of a general framework for second-level adaptive prediction which is formed from a group of predictors. It is a natural extension to that of the first-level which is formed directly from a group of pixels. The proposed framework offers a greater degree of freedom for adaptation and addresses some of the tough problems such as model uncertainty that is inherent to the first-level prediction methods. We show that the proposed methods of taking weighted average (WAVE) and weighted median (WMED) of a group of predictions are alternative and competitive adaptive image prediction methods. We have achieved better compression performance than that of TMW/sup Lego/ by combining a group of linear predictors.