Homography-guided stereo matching for wide-baseline image interpolation

Image interpolation has a wide range of applications such as frame rate-up conversion and free viewpoint TV. Despite significant progresses, it remains an open challenge especially for image pairs with large displacements. In this paper, we first propose a novel optimization algorithm for motion estimation, which combines the advantages of both global optimization and a local parametric transformation model. We perform optimization over dynamic label sets, which are modified after each iteration using the prior of piecewise consistency to avoid local minima. Then we apply it to an image interpolation framework including occlusion handling and intermediate image interpolation. We validate the performance of our algorithm experimentally, and show that our approach achieves state-of-the-art performance.

High Capacity Reversible Steganography on CMY and HSI Color Images Using Image Interpolation

Steganography using image interpolation has created a new research area in multimedia communication. A reversible data concealing in HSI and CMY color models using image interpolation is proposed in this paper. The HSI and CMY image models are interpolated using High Capacity Reversible Steganography (CRS) technique. The median plane of both HSI and CMY color models are selected for secret message bit concealing. The secret message bits are concealed in the cover plane by Exclusive OR (XOR) operation. Since the cover image is recovered after secret message bit retrieval, this finds application in military and medical imaging applications. The experimental results of proposed scheme showed very high embedding capacity of about 16 bits in each pixel location of calculated pixel value, good image quality with a surface similarity index measure (SSIM) value 1 and high PSNR. Also, high robustness is achieved on comparing with the existing works.

Medical Image Interpolation Using Recurrent Type-2 Fuzzy Neural Network

Image interpolation is an essential process for image processing and computer graphics in wide applications to medical imaging. For image interpolation used in medical diagnosis, the two-dimensional (2D) to three-dimensional (3D) transformation can significantly reduce human error, leading to better decisions. This research proposes the type-2 fuzzy neural networks method which is a hybrid of the fuzzy logic and neural networks as well as recurrent type-2 fuzzy neural networks (RT2FNNs) for advancing a novel 2D to 3D strategy. The ability of the proposed methods in the approximation of the function for image interpolation is investigated. The results report that both proposed methods are reliable for medical diagnosis. However, the RT2FNN model outperforms the type-2 fuzzy neural networks model. The average squares error for the recurrent network and the typical network reported 0.016 and 0.025, respectively. On the other hand, the number of fuzzy rules for the recurrent network and the typical network reported 16 and 22, respectively.

Image Inpainting Using Image Interpolation - An Analysis

Image inpainting is a computer technique of filling lost regions of an image by using available information from the surrounding area. This digital image inpainting technique has wider applications like image restoration, dis-occlusion and image/video compression. The traditional image inpainting approaches Partial Differential Equation (PDE) based method and Exemplar based method mainly focus on the size of the target region to be filled. PDE is a pixel oriented method and works well if the region to be filled is small. On the other hand exemplar based method is a patch based method and works well if the region to be filled is large. This paper proposes another new technique called image interpolation technique and describes how this technique can be used effectively for image inpainting without giving much focus on the size of the target region.