Image resizing using fuzzy inferences

Abstract

Here, the authors present a fuzzy-based approach for image resizing. The authors introduce a new approach for improving the gradient map and saliency map. Authors’ approach first constructs three different sizes of structural elements, which are used for the operation of close–open filtering to process the input image to obtain three smooth images. Then, the authors use edge detection to process these images, and merge them to obtain an improved gradient map. Besides, a saliency map of the input image is calculated. The authors use hedges in fuzzy logic to strengthen the values of the significant pixels and reduce the values of the background pixels. Hence, the authors can obtain an improved saliency map. After that, the authors introduce a technique to generate a weighted map and then to obtain weighted gradient and saliency maps. Finally, the authors use fuzzy-based approach combining the weighted gradient and saliency maps to obtain an importance map. The map is used when the authors use the seam carving to adjust image size. Experimental results show authors’ approach using the importance map can produce better image resizing results.