Sascha D. Cvetkovic

Improved Nonlocal Means Based on Pre-Classification and Invariant Block Matching

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.

Tone-mapping functions and multiple-exposure techniques for high dynamic-range images

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.

Image enhancement circuit using nonlinear processing curve and constrained histogram range equalization

For real-time imaging in surveillance applications, image fidelity is of primary importance to ensure customer confidence. The obtained image fidelity is a result from amongst others dynamic range expansion and video signal enhancement. The dynamic range of the signal needs adaptation, because the sensor signal has a much larger range than the standard CRT display. The signal enhancement should accommodate for the widely varying light and scene conditions and user scenarios of the equipment. This paper proposes a new system to combine dynamic range and enhancement processing, offering a strongly improved picture quality for surveillance applications. The key to our solution is that we use Non-Linear Processing (NLP) with a so-called Constrained Histogram Range Equalization (CHRE). The NLP transforms the digitized high-dynamic luminance sensor signal such that details of the low-luminance parts are enhanced, while avoiding detail losses in the high-luminance areas. The CHRE technique enhances visibility of the global contrast for the camera signal without significant information loss in the statistically less relevant areas. Evaluations of this proposal have shown clear improvements of the perceptual image quality. An additional advantage is that the new scheme is adaptable and allows the concatenation of further enhancement techniques without sacrificing the obtained picture quality improvement.

Locally-Adaptive Image Contrast Enhancement without Noise and Ringing Artifacts

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.

Non-linear locally-adaptive video contrast enhancement algorithm without artifacts

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.

Efficient Operator for Local Energy Measurements to Enhance an Image Signal

For real-time imaging with digital video cameras and high-quality display with TV systems, the obtained picture quality including visibility of details, local contrast, and absence of artifacts, is very important to ensure user quality acceptance. We present a multi-window real-time high-frequency enhancement scheme, in which gain is a non-linear function of the detail energy. Then we discuss the computation of commonly used local energy measurements and show that a selection of those measurements can be calculated efficiently. In our first contribution, we propose a new local energy measurement APS that can be calculated more efficiently than the existing metrics in a 2-D-separable fashion. In addition, we also show that the new APS measurement gives better performance than standard energy measurements. The second contribution is the use of local contrast and a modified contrast gain formula that can substantially improve the overall algorithm performance, especially when a high-level contrast enhancement is desired. Our algorithm trades off between added contrast and “halo” artifacts, resulting in a good balance between visibility of details and an acceptable level of artifacts. The new scheme can be successfully applied to cameras and TV systems to improve their visual quality.

Automatic level control for video cameras towards HDR techniques

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.

Multi-band locally-adaptive contrast enhancement algorithm with built-in noise and artifact suppression mechanisms

For real-time imaging in surveillance applications, visibility of details is of primary importance to ensure customer confidence. 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 non-linear function of the detail energy. Our algorithm simultaneously 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 detail gains can be set much higher than usual and the algorithm will reduce them only at places where it is really needed.