Image Enhancement with Statistical Estimation

Contrast enhancement is an important area of research for the image analysis. Over the decade, the researcher worked on this domain to develop an efficient and adequate algorithm. The proposed method will enhance the contrast of image using Binarization method with the help of Maximum Likelihood Estimation (MLE). The paper aims to enhance the image contrast of bimodal and multi-modal images. The proposed methodology use to collect mathematical information retrieves from the image. In this paper, we are using binarization method that generates the desired histogram by separating image nodes. It generates the enhanced image using histogram specification with binarization method. The proposed method has showed an improvement in the image contrast enhancement compare with the other image.Comment: 9 pages,6 figures; ISSN:0975-5578 (Online); 0975-5934 (Print

Application of Threshold Techniques for Readability Improvement of Jawi Historical Manuscript Images

Historical documents such as old books and manuscripts have a high aesthetic value and highly appreciated. Unfortunately, there are some documents cannot be read due to quality problems like faded paper, ink expand, uneven colour tone, torn paper and other elements disruption such as the existence of small spots. The study aims to produce a copy of manuscript that shows clear wordings so they can easily be read and the copy can also be displayed for visitors. 16 samples of Jawi historical manuscript with different quality problems were obtained from The Royal Museum of Pahang, Malaysia. We applied three binarization techniques; Otsu's method represents global threshold technique; Sauvola and Niblack method which are categorized as local threshold techniques. We compared the binarized images with the original manuscript to be visually inspected by the museum's curator. The unclear features were marked and analyzed. Most of the examined images show that with optimal parameters and effective pre processing technique, local thresholding methods are work well compare with the other one. Niblack's and Sauvola's techniques seem to be the suitable approaches for these types of images. Most of binarized images with these two methods show improvement for readability and character recognition. For this research, even the differences of image result were hard to be distinguished by human capabilities, after comparing the time cost and overall achievement rate of recognized symbols, Niblack's method is performing better than Sauvola's. We could improve the post processing step by adding edge detection techniques and further enhanced by an innovative image refinement technique and a formulation of a class proper method.Comment: 10 pages, 6 figures, 2 tables, Advance Computing: An International Journal (ACIJ

DeepOtsu: Document Enhancement and Binarization using Iterative Deep Learning

This paper presents a novel iterative deep learning framework and apply it for document enhancement and binarization. Unlike the traditional methods which predict the binary label of each pixel on the input image, we train the neural network to learn the degradations in document images and produce the uniform images of the degraded input images, which allows the network to refine the output iteratively. Two different iterative methods have been studied in this paper: recurrent refinement (RR) which uses the same trained neural network in each iteration for document enhancement and stacked refinement (SR) which uses a stack of different neural networks for iterative output refinement. Given the learned uniform and enhanced image, the binarization map can be easy to obtain by a global or local threshold. The experimental results on several public benchmark data sets show that our proposed methods provide a new clean version of the degraded image which is suitable for visualization and promising results of binarization using the global Otsu's threshold based on the enhanced images learned iteratively by the neural network.Comment: Accepted by Pattern Recognitio

The Impact of Different Image Thresholding based Mammogram Image Segmentation- A Review

Images are examined and discretized numerical capacities. The goal of computerized image processing is to enhance the nature of pictorial data and to encourage programmed machine elucidation. A computerized imaging framework ought to have fundamental segments for picture procurement, exceptional equipment for encouraging picture applications, and a tremendous measure of memory for capacity and info/yield gadgets. Picture segmentation is the field broadly scrutinized particularly in numerous restorative applications and still offers different difficulties for the specialists. Segmentation is a critical errand to recognize districts suspicious of tumor in computerized mammograms. Every last picture have distinctive sorts of edges and diverse levels of limits. In picture transforming, the most regularly utilized strategy as a part of extricating articles from a picture is "thresholding". Thresholding is a prevalent device for picture segmentation for its straightforwardness, particularly in the fields where ongoing handling is required