Adaptive Texts Deconvolution Method for Real Natural Images
Le Thi Thanh, Dang N. H. Thanh, V. B. Surya Prasath · 2019
Understanding of real scenes is an important task in augmented reality (AR). Identifying and comprehension of texts from real scene images is useful in implementing robust AR devices. Therefore, improving the quality of text images for better readability is very important and text images deconvolution is useful to increase the accuracy of AR pattern recognition algorithms. In this work, we propose an estimation method for the filtering operator within total variation deconvolution model. This method is applied for the texts image deconvolution problem from natural images. In the experiments, we use NIQE score - the blind quality assessment metric - to assess the deconvolution quality. We further compare the proposed method with other image deconvolution models such as the blind deconvolution, the Lucy and the Wiener methods. The experimental indicate that our deconvolution method works effectively for texts enhancement across different scenes with high quality results.