Video enhancement is one of the most important and difficult components in video research. The aim of video enhancement is to improve the visual appearance of the video, or to provide a "better"transform representation for future automated video processing, such as analysis, detection, segmentation, recognition, surveillance, traffic, criminal justice systems. In this paper, we present an overview of video enhancement processing and analysis algorithms used in these applications. The existing techniques of video enhancement can be classified into two categories: Self-enhancement and Context- based fusion enhancement. More specifically, we categorize processing methods based representative techniques of video enhancement. Thus, the contribution of the paper has fourfold: (1) to classify and review video enhancement processing algorithms, (2) to discuss the advantages and disadvantages of these algorithms, (3) according to this integrated consideration, attempt an evaluation of shortcomings and general needs in this field of active research, and (4) we will point out promising directions on research for video enhancement for future research.

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A Survey of Video Enhancement Techniques

This study addresses low dynamic range (LDR) image/video enhancement for digital video cameras. A new fast dynamic range compression format with a local-contrast-preservation (FDRCLCP) algorithm resolves this problem efficiently. The proposed FDRCLCP algorithm can combine with any continuously differentiable intensity transfer function to achieve LDR image enhancement. In combination with the FDRCLCP algorithm, a new intensity-transfer function is proposed, adaptively accomplishing dynamic range compression and edge-contrast enhancement depending on the local mean value of the input luminance image. The proposed method also extends to a linear color remapping approach, not only preserving the original image's color information, but also controlling color saturation of the resulting image. Moreover, a look-up-table (LUT) acceleration approach improves the processing speed of the proposed FDRCLCP algorithm in processing video signals, allowing real-time video enhancement processing. Experimental results show that the proposed method not only provides good visual representation in both quantitative and visual comparisons, but also achieves real-time performance for video processing.

A Fast Dynamic Range Compression With Local Contrast Preservation Algorithm and Its Application to Real-Time Video Enhancement

In this paper, a novel approach for noise reduction and enhancement of extremely low-light video is proposed. For noise removal, a motion adaptive temporal filtering based on a Kalman structured updating is presented. Dynamic range of denoised video is increased by adjustment of RGB histograms using Gamma correction with adaptive clipping thresholds. Finally, residual noise is removed using a nonlocal means (NLM) denoising filter. The proposed method works directly on the color filter array (CFA) raw video for achieving low memory consumption1.

A novel approach for denoising and enhancement of extremely low-light video

This article addresses the problem of low dynamic range image enhancement for commercial digital cameras. A novel simultaneous dynamic range compression and local contrast enhancement algorithm (SDRCLCE) is presented to resolve this problem in a single-stage procedure. The proposed SDRCLCE algorithm is able to combine with many existent intensity transfer functions, which greatly increases the applicability of the proposed method. An adaptive intensity transfer function is also proposed to combine with SDRCLCE algorithm that provides the capability to adjustably control the level of overall lightness and contrast achieved at the enhanced output. Moreover, the proposed method is amenable to parallel processing implementation that allows us to improve the processing speed of SDRCLCE algorithm. Experimental results show that the performance of the proposed method outperforms three state-of-the-art methods in terms of dynamic range compression and local contrast enhancement.

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A novel simultaneous dynamic range compression and local contrast enhancement algorithm for digital video cameras

Contrast enhancement is crucial to the domain of security and surveillance where limitations in dynamic range and lack of lighting sources prevent fine details of the scene from being captured. Here, we propose a method of nighttime video contrast enhancement based on genetic algorithms. Conversion from RGB to HSI and illumination component extraction were done firstly. Illumination-based enhancement which combines chromosome, corresponding operators and genetic algorithm was then applied to enhance the contrast and details of the video according to an objective fitness criterion. Image reconstruction followed previous procedures finally. Comparison of our proposed method with other automatic enhancement techniques such as histogram equalization shows that our method produces natural looking images/videos, especially when the dynamic range of the input image is high. Results obtained, both in terms of subjective and objective evaluation, show the superiority of the proposed method.

Illumination-based nighttime video contrast enhancement using genetic algorithm

This paper presents a novel video enhancement system based on an adaptive spatio-temporal connective (ASTC) noise filter and an adaptive piecewise mapping function (APMF). For ill-exposed videos or those with much noise, we first introduce a novel local image statistic to identify impulse noise pixels, and then incorporate it into the classical bilateral filter to form ASTC, aiming to reduce the mixture of the most two common types of noises—Gaussian and impulse noises in spatial and temporal directions. After noise removal, we enhance the video contrast with APMF based on the statistical information of frame segmentation results. The experiment results demonstrate that, for diverse low-quality videos corrupted by mixed noise, underexposure, overexposure, or any mixture of the above, the proposed system can automatically produce satisfactory results.

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Yi-Ming liu

Papers

Video Enhancement Using Adaptive Spatio-Temporal Connective Filter and Piecewise Mapping