Contrast enhancement of night images

Night images obtained from a surveillance camera have low visibility compared to daytime images. Images captured at night have low brightness, low contrast and high noise. A modified contrast enhancement (CE) algorithm was proposed and developed in luminance-chrominance space. Only the luminance channel obtained by PCA transform is processed as it contains the most valuable information. Daytime images are simulated with various degrees of contrast and Poisson noise using MATLAB. CE algorithm is applied in three scales to obtain good brightness and contrast of the images. Images are denoised by using bilateral filter that smoothes the noise while preserving edges. The brightness and contrast of the night images have been enhanced significantly and the noise is reduced effectively, preserving the details of the images. Finally, the performance of the proposed algorithm is illustrated by processing images under various lighting conditions without introducing halo and ghosting artifacts. Structural similarity and visual contrast measures demonstrate that the proposed method is more effective over other existing methods.

[1]  Takeo Kanade,et al.  Denighting: Enhancement of nighttime images for a surveillance camera , 2008, 2008 19th International Conference on Pattern Recognition.

[2]  Juan Song,et al.  Enhancement and noise reduction of very low light level images , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[3]  G. Buchsbaum,et al.  Trichromacy, opponent colours coding and optimum colour information transmission in the retina , 1983, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[4]  Seong-Won Lee,et al.  Noise-adaptive spatio-temporal filter for real-time noise removal in low light level images , 2005, IEEE Trans. Consumer Electron..

[5]  Byungyong Ryu,et al.  Content-Aware Dark Image Enhancement Through Channel Division , 2012, IEEE Transactions on Image Processing.

[6]  Yücel Altunbasak,et al.  A Histogram Modification Framework and Its Application for Image Contrast Enhancement , 2009, IEEE Transactions on Image Processing.

[7]  Nikolay N. Ponomarenko,et al.  TID2008 – A database for evaluation of full-reference visual quality assessment metrics , 2004 .

[8]  Pallavi H. Yawalkar,et al.  A Review on Low Light Video Enhancement Using Image Processing Technique , 2015 .

[9]  Laurence Meylan,et al.  High dynamic range image rendering with a retinex-based adaptive filter , 2006, IEEE Transactions on Image Processing.

[10]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[11]  Calle Lejdfors,et al.  Adaptive enhancement and noise reduction in very low light-level video , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[12]  Xiao-Ping Zhang,et al.  A variational framework for single low light image enhancement using bright channel prior , 2013, 2013 IEEE Global Conference on Signal and Information Processing.

[13]  P. V. Ingole,et al.  Low Light Video Enhancement: A Survey , 2015 .

[14]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[15]  Yeong-Ho Ha,et al.  Local Contrast Enhancement Based on Adaptive Multi-Scaled Retinex using Intensity Distribution of Input Image , 2010, Color Imaging Conference.

[16]  Leiting Chen,et al.  A Survey of Video Enhancement Techniques , 2012, J. Inf. Hiding Multim. Signal Process..