Region of Interest Detection in Low Light Images Using Visual Attention Models
Aiming at the difficulty of detection caused by low-light images,this paper put forward a method for detecting the regions of interest in low-light image based on visual attention model.First of all,the image was conver-ted from RGB color space to HSV color space,and then multiple high-pass sub bands and a low-pass sub-band were obtained by NSST.Moreover,adaptive threshold denoising method was adopted to denoise the V component in the high-pass sub-bands.Meanwhile,multi-scale Retinex was used to enhance the V component in the low-pass sub-band,and then correct the S component in the enhanced image.After that,the image was converted back to RGB color space.Furthermore,saliency value of brightness,color saliency value and directional saliency value were obtained re-spectively according to the visual attention model.Finally,feature vectors of image pixel were constructed jointly,and then the transition-sliding window Bayesian method was adopted to detect the region of interest in the image.The ex-perimental results show that the proposed method has more ideal preprocessing effect as well as lower the misclassifi-cation rate and the classification error rate.
Visual attention modelLow-light imageDetection for region of interestTransition-sliding window Bayesian
NETL
NSTL
万方数据
