One of the most popular image denoising methods based on self-similarity is called nonlocal means (NLM). Though it can achieve remarkable performance, this method has a few shortcomings, e.g., the computationally expensive calculation of the similarity measure, and the lack of reliable candidates for some nonrepetitive patches. In this paper, we propose to improve NLM by integrating Gaussian blur, clustering, and rotationally invariant block matching (RIBM) into the NLM framework. Experimental results show that the proposed technique can perform denoising better than the original NLM both quantitatively and visually, especially when the noise level is high.

For real-time imaging with digital video cameras and high-quality with TV display systems, good tonal rendition of video is important to ensure high visual comfort for the user. Except local contrast improvements, High Dynamic Range (HDR) scenes require adaptive gradation correction (tone-mapping function), which should enable good visualization of details at lower brightness. We discuss how to construct and control improved tone-mapping functions that enhance visibility of image details in the dark regions while not excessively compressing the image in the bright image parts. The result of this method is a 21-dB expansion of the dynamic range thanks to improved SNR by using multiple- exposure techniques. This new algorithm was successfully evaluated in HW and outperforms the existing algorithms with 11 dB. The new scheme can be successfully applied to cameras and TV systems to improve their contrast.

We give a comprehensive overview of the complete exposure processing chain for video cameras. For each step of the automatic exposure algorithm we discuss some classical solutions and propose their improvements or give new alternatives. We start by explaining exposure metering methods, describing types of signals that are used as the scene content descriptors as well as means to utilize these descriptors. We also discuss different exposure control types used for the control of lens, integration time of the sensor, and gain control, such as a PID control, precalculated control based on the camera response function, and propose a new recursive control type that matches the underlying image formation model. Then, a description of commonly used serial control strategy for lens, sensor exposure time, and gain is presented, followed by a proposal of a new parallel control solution that integrates well with tone mapping and enhancement part of the image pipeline. Parallel control strategy enables faster and smoother control and facilitates optimally filling the dynamic range of the sensor to improve the SNR and an image contrast, while avoiding signal clipping. This is archived by the proposed special control modes used for better display and correct exposure of both low-dynamic range and high-dynamic range images. To overcome the inherited problems of limited dynamic range of capturing devices we discuss a paradigm of multiple exposure techniques. Using these techniques we can enable a correct rendering of difficult class of high-dynamic range input scenes. However, multiple exposure techniques bring several challenges, especially in the presence of motion and artificial light sources such as fluorescent lights. In particular, false colors and light-flickering problems are described. After briefly discussing some known possible solutions for the motion problem, we focus on solving the fluorescence-light problem. Thereby, we propose an algorithm for the detection of fluorescent lights from the image itself and define a set of remedial actions, to minimize false color and light-flickering problems.

For real-time imaging with digital video cameras and high-quality display with TV systems, visibility of details is very important to ensure user quality acceptance. Many complex scenes require local contrast improvements that should bring details to the best possible visibility. However, local enhancement methods mainly suffer from ringing artifacts and over-emphasizing noise. We present a multi-window real-time high-frequency enhancement scheme, in which gain is a non-linear function of the detail energy. Our algorithm controls perceived sharpness, ringing artifacts (contrast) and noise, resulting in a good balance between visibility of details and non-disturbance of artifacts. The overall quality enhancement is based on a careful selection of the filter types for the multi-band decomposition and a detailed analysis of the signal per frequency band. The advantage of the proposed technique is that details in images become more visible without introduction of annoying artifacts. The new scheme can be successfully applied to cameras and TV systems to improve their contrast.

For real-time imaging in surveillance applications, visibility of details is of primary importance to ensure customer confidence. Additional constraints are the absence of human interaction and low computational complexity. Usually, image quality is improved by enhancing contrast and sharpness. Many complex scenes require local contrast improvements that should bring details to the best possible visibility. However, local enhancement methods mainly suffer from ringing artifacts and noise over-enhancement. In this paper, we present a new multi-window real-time high-frequency enhancement scheme, in which gain is a nonlinear function of the detail energy. Our algorithm controls perceived sharpness, ringing artifacts (contrast) and noise, resulting in a good balance between visibility of details and non-disturbance of artifacts. Its advantage is that gains can be set now much higher than usual and the algorithm will reduce them only at places where it is really needed.

This paper describes a background-subtraction system with light change-detection which works on a luminance QCIF-size video signal for surveillance applications. The new proposed pixel background model is controlled by a statistical threshold and is robust for cluttered background and small object motions. Moreover, (or light-change detection, we introduce temporal prediction of pixel values to estimate trends while quickly adapting to scene changes to facilitate a very sensitive detection of moving targets. Experiments show that a local contrast enhancement applied prior to down-sampling improves detection sensitivity, arid combined with the shifted sealed difference and me Wronskian determinant operators provides the best background/foreground detection.