Research On Key Techniques Of Nighttime Video Enhancement

Video information is used to recognition and identifies objects in daily, addressactual application problems. However, the captured nighttime videos are often too darkor non-clear for monitoring purposes due to the extremely weather condition, poorlighting conditions and the relatively low-cost cameras used, and nighttime videos don'tfit surveillance and satisfaction applications. In order to address above problems, weresearch nighttime video enhancement techniques from self-enhancement andillumination-based frame fusion and related techniques of fusion enhancement. In thisdissertation, we firstly analyze video enhancement related techniques, and proposegeneral framework of nighttime video enhancement, then analyze video enhancementtechniques, we propose several algorithms of nighttime video enhancement, at last, wepropose GME algorithm to resolve camera motion problem which is exiting nighttimeenhancement preprocessing.The main contributions in this dissertation are summarized as follows:(1) We present an overview of video enhancement processing and analysisalgorithms used in these applications. The existing techniques of video enhancementcan be classified into two categories:â‘ Self-enhancement,â‘¡Illumination-based framefusion enhancement. More specifically, based on discussing the advantages anddisadvantages of these algorithms, evaluation approaches of nighttime videoenhancement algorithms are proposed. Illumination-based enhancement of nighttimevideo analysis, a general framework of nighttime video enhancement is proposed andalso analyzes the proposed framework techniques.(2) We analyze several problems of existing techniques for nighttime videoenhancement. In this dissertation, an enhancement algorithm for nighttime videosurveillance applications based on illumination fusion is proposed, which fuses videoframes from daytime backgrounds and nighttime video. The main contributions of theproposed algorithm are summarized as follows: the proposed algorithm uses an additiveenhanced "Term" with foreground object extraction to enhance nighttime videos andobjects, to make up what existing algorithms have problems. To avoid light-inversion and sensitivity problems and to reduce ghost patterns introduced by illumination ratiovariations, a constrained low-passed filter is proposed in enhanced nighttime videosprocess.(3) We discuss several problems of the existing techniques for nighttime videoenhancement. We propose a novel and effective nighttime video enhancement algorithmfor video surveillance applications by using illumination compensation which fusesvideo frames from high quality daytime backgrounds and low quality nighttime video.For further improving the perceptual quality of the moving objects, an algorithm basedon object region ratio average is also proposed.(4) The traditional image enhancement algorithm of intensity-based is applicated tocolor videos, which enhanced color will not to garmonize with original video at all anddestroy nature color balance. In order to address this problem, we propose an efficientcontrast enhancement algorithm based on genetic algorithm (GA). The proposedalgorithm illumination-based is processed to address color garmonize problem.(5) Due to non-subsampled contourlet transform (NSCT) has translation invariantproperty and can control noise in a certain extent, we propose NSCT-based nighttimevideo enhancement algorithms. The proposed algorithm use daytime backgroundillumination fusing nighttime video frame illumination to enhance nighttime videos. Inthis work, we focus on address two problems:â‘ the proposed NSCT-based algorithmfuse the same scene of daytime background and nighttime video frames.â‘¡based thisanalysis, for further improving the perceptual quality of the moving object, we proposean improved framework for nighttime video enhancement which can efficiently recoverthe unreasonable enhanced results dues to imperfect moving objects extraction.(6) In order to enhance nighttime video, usually we use external daytime orhigh-quality images of the same scene to help enhance the nighttime videos, however,the surveillance camera may often have tiny motions which results in scene differencesbetween daytime and nighttime videos. In these cases, the previous methods may oftenlose static illumination and create unreasonable results. Based on this, we propose aglobal-motion-estimation-based scheme to address the problem of scene differencesbetween daytime and nighttime videos. At the same time, we further propose animproved framework for nighttime video enhancement which can efficiently recover theunreasonable enhanced results due to scene difference.