Development Of Contrast Enhancement Method For Digital Images

Photos captured in the dark environments, which have insufficient or uneven lighting conditions, might lead to low contrast images. The night images are looked dark and not clear as compared to day images. Image enhancement methods can be applied to improve the image quality. Histogram equalization (HE) method is a common image enhancement method. Although researchers had proposed many enhancement methods which including global and local histogram equalization, there are still some problems faced which include over enhancement, shift of mean brightness and loss of details. Hence, two image enhancement methods were developed by cascading exposure sub-image histogram equalization (ESIHE) and contrast limited adaptive histogram equalization (CLAHE) in different sequences. ESIHE is a global histogram equalization based method, while CLAHE is a local histogram equalization based method. Then, these two proposed methods were compared with existing HE based methods qualitatively and quantitatively. The qualitative assessment is visual assessment survey, while quantitative assessments are. noise standard deviation (NSD), image variance (IV), speckle index (SI) and contrast per pixel (CPP). Based on the assessments, the method that applied ESIHE then followed by CLAHE is able to enhance images better than the method applied CLAHE first and followed by ESIHE. The output image have a natural appearance, high contrast, and the details of image are clear

Contrast Enhancement Algorithm Based on Gap Adjustment for Histogram Equalization

Image enhancement methods have been widely used to improve the visual effects of images. Owing to its simplicity and effectiveness histogram equalization (HE) is one of the methods used for enhancing image contrast. However, HE may result in over-enhancement and feature loss problems that lead to unnatural look and loss of details in the processed images. Researchers have proposed various HE-based methods to solve the over-enhancement problem; however, they have largely ignored the feature loss problem. Therefore, a contrast enhancement algorithm based on gap adjustment for histogram equalization (CegaHE) is proposed. It refers to a visual contrast enhancement algorithm based on histogram equalization (VCEA), which generates visually pleasing enhanced images, and improves the enhancement effects of VCEA. CegaHE adjusts the gaps between two gray values based on the adjustment equation, which takes the properties of human visual perception into consideration, to solve the over-enhancement problem. Besides, it also alleviates the feature loss problem and further enhances the textures in the dark regions of the images to improve the quality of the processed images for human visual perception. Experimental results demonstrate that CegaHE is a reliable method for contrast enhancement and that it significantly outperforms VCEA and other methods