Image Enhancement Algorithm Based On Adaptive Threshold

Image enhancement is an important part of digital image processing. It plays a key role in improving image quality. Images taken by devices often have problems of low quality, such as blur and low contrast. In this case, image enhancement is necessary to make the interested parts of images better performed.After long time development,, many typical image enhancement methods are available now. For example, histogram equalization, median filtering, transform domain enhancement, etc. Generally speaking, certain enhancement methods are used only in specific conditions, not in all situations.Concepts and some theoretical basis about image enhancement methods are explained firstly. Then, some representative algorithms are deeply analyzed, including grey level transformation, histogram equalization and wavelet enhancement.After that, a new threshold method is proposed on the basis of wavelet analysis. Traditional linear enhancement method based on wavelet often causes over-enhancement problem for images. But enhancement algorithm based on adaptive threshold can avoid that problem. In this algorithm, an image is first decomposed to get wavelet coefficients. Then, optimal threshold is obtained according to statistical information of low frequency wavelet coefficients. Then low frequency coefficients are processed according to the threshold. Finally, wavelet reconstruction is operated to obtain enhanced image. Experiments show that this algorithm has excellent enhancement effect for low-contrast images.In consideration of the effect of geometric distortion on image quality, geometric correction methods discussed afterwards. These methods are effective in improve images visual effect. Also, a new mathematical model method is proposed to solve the problem. First, text lines are iterated through to find points that mark words position. Then, position points are fitted according model to find optimum curves. Finally, text pixels are recovered according to the difference between fitting curve and horizontal line. Experiment results show that distorted document images are correctly recovered with good time efficiency.