Technical limitations in image capturing usually impose defect, such as contrast degradation. There are different approaches to improve the contrast of an image. Among the exiting approaches, un-sharp Masking is a popular method due to its simplicity in implementation and computation. There is an important parameter in un-sharp Masking, named gain factor, which affects the quality of the enhanced image. In this paper, a new adaptive un-sharp Masking method is proposed. In the proposed method gradient variation of the image is used to estimate the gain factor for un-sharp Masking. Gradient variation of an image can provide information about the image contrast. Subjective and objective image quality assessments are used to compare the performance of the proposed method both with the classic and the recently developed un-sharp Masking methods. The experimental results show the superiority of the proposed method compared to the existing methods in image enhancing using un-sharp Masking.

Un-sharp Masking method improves the images contrast without requiring any prior knowledge. In this method, a sharper image can be achieved by empowering the high frequency components of the input image. Un-sharp Masking has a parameter named gain factor which has a high effect on the enhanced image quality. In this paper, an approach is proposed to adaptively estimate the appropriate value of this parameter in order to effectively enhance an image with local blur, or an image with non-uniform blur. In proposed method, first, the input image is segmented into blur and non-blur regions. Then the gain factor is estimated for each region adaptively. In this approach, the influence of the image blurriness on its gradient information is used to estimate the value for the gain factor. The image quality assessments are applied to evaluate the performance of proposed un-sharp Masking method in image enhancement. Experimental results demonstrate that the performance of our proposed method is better than the performance of existing un-sharp Masking methods in image enhancement.

Background and Aim: In noisy environments, two types of Masking including energetic mask-ing (EM) and informational Masking (IM) occur. EM results from the spectral overlap of the target and maskers on the basilar membrane, while IM occurs at higher level. This paper aimed to rev-iew the concept of IM in terms of historical perspective and definitions, the important cues for releasing from it, age-related effects and its neural basis. Recent Findings: The data from psychoacoustic, behavioral, and neuro-imaging studies were revi-ewed and discussed in order to provide an overall image of IM. According to these studies, it seems that perceptual segregation between the target and maskers is the most important cues for relea-sing from IM. This process takes place simply and without any effort in adults with normal hearing; however, it does not occur easily in chil-dren, elderly people and those with impaired hearing. Moreover, it seems that both top-down and bottom-up processing are involved in IM for-mation. Conclusion: Since IM leads to failure in selec-tion of auditory objects and prevents the indivi-dual from auditory scene analysis, understanding the IM concept leads to a better knowledge of speech perception in noise.