Juhi Bhadviya

Bio: Juhi Bhadviya is an academic researcher from LNM Institute of Information Technology. The author has contributed to research in topics: Stairstep interpolation & Interpolation. The author has an hindex of 1, co-authored 7 publications receiving 4 citations.

A switching based adaptive image interpolation algorithm

Abstract: In this paper, we propose a new adaptive image interpolation algorithm for enhancement of natural images. The proposed method uses different algorithms namely SAI, SPIA and Context-Based Image Interpolation Algorithm (CBIA) techniques, for both edgy and smooth type of images. The detailed part of smooth type image is interpolated by SAI, while we propose to use SPIA method for detailed part of edgy image. The rest of the pixels for either type of images are interpolated by CBIA. From the simulation results, we found that our adaptive interpolation technique results in better subjective and objective (PSNR) quality in comparision to some of the recent works in literature.

An efficient 2-D jacobian iteration modeling for image interpolation

Abstract: This paper proposes a new interpolation approach for obtaining high resolution (HR) images from its low resolution (LR) images. We are using the Least Squared based block by block prediction scheme to estimate the predictors using Jacobian iteration method. In spite of Jacobian's Iterative property of convergence for diagonally dominant matrices only, our proposed method uses this property effectively for all types of matrices, and found a set of prediction coefficients using a small number of iterative steps. Due to its lesser computational cost it can be used in real time applications too. Use of iterative methods like Jacobi gives an advantage of its application over images which gives singular matrices during operation. Experimental results indicates that the proposed algorithm gives better quantitative performance as compared to other conventional interpolation techniques.

An efficient new edge preserving technique for removal of salt and pepper noise

Abstract: This paper proposes an efficient procedure for removal of salt and pepper noises from the noisy images on the basis of their local edge preserving filters. This algorithm consists of two major stages. In the first stage, the maximum and minimum pixel value in the the corrupted image is used to select noisy pixels or noise free pixels and then in second stage, local edge preserving filters are used on the basis of noisy pixel detected and the nature of its neighboring pixels in the selected window. Comparing the obtained results with other computationally simple noise removal techniques, our proposed algorithm gives much better qualitative and quantitative performance. Due to its simplicity and low computational cost, our method is suitable for its application in many real time situations.

Effects of different prediction schemes on lossless invisible watermarking

Abstract: In view of high data embedding capacity and many real time applications, we have proposed a reversible invisible watermarking algorithm. The technique is used to embed a set of watermark data in an image using a one pass embedding process and later recovering the original image without any loss, after the extraction of watermark. Main motivation behind this work is the usage of two different prediction schemes-Weighted Causal Average and Context Based Image Compression Algorithm, to obtain two images similar to the original image, and using the error pattern resulted from the two predicted images to embed data depending upon the nature of image. Based on this error pattern, binary data is embedded in an image using two different embedding schemes- Bijective Mirror Mapping technique and Histogram Shifting Algorithm, resulting in better embedding capacity or payload capacity and better PSNR than other reversible one-pass watermarking algorithms mentioned in literature.

An efficient two phase image interpolation algorithm based upon error feedback mechanism

Abstract: Many image interpolation algorithms have been developed in the recent past aiming for high prediction accuracy. But these algorithms are focused only towards better predictor design. In this paper, we propose a generic two phase image interpolation algorithm based upon error feedback mechanism. In the first phase, we learn error pattern occurred during interpolation of down sampled version of original Low Resolution (LR) image. It is assumed that similar error pattern also occurrs during the interpolation of original LR image. Hence, error pattern learnt in first phase, is employed during the interpolation of original LR image (second phase). From extensive experiments, we found that our algorithm gives a significant improvement in prediction accuracy of existing interpolation algorithms. In particular, our algorithm plays a significant role in improving prediction accuracy of those algorithms which have inherently poor prediction capability for certain types of images.

A Relook and Renovation over State-of-Art Salt and Pepper Noise Removal Techniques

Abstract: Salt and pepper noise is a type of impulse noise, where certain amount of black and white dots appear in the image. The intensity is accumulated in 8 bit integer, giving 256 possible gray levels in the range (0 – 255).In this range salt and pepper noise takes either minimum or maximum intensity. Positive impulse appears as white (salt) points with intensity 255‘ and negative impulse appears as black (pepper) points with intensity 0‘ respectively. Salt and pepper noise removal is not an easy task mostly when noise density in the contaminated image is high and restoration of image quality is essential. Different filters like MF, SMF, AMF, PSMF, DBA, DBUTMF, and MDBUTMF and so on are noticed useful for taking away low, moderate and high density salt and pepper noise. The purpose of this paper is to present these filters first and then revise their art to enhance their performances and usefulness. The comparison shows that some of these filters are very fruitful in some particular noise density levels and hence classified applications on these situations are recommended based on the output of investigations.

A novel adaptive image zooming scheme via weighted least-squares estimation

Abstract: A critical issue in image interpolation is preserving edge detail and texture information in images when zooming. In this paper, we propose a novel adaptive image zooming algorithm using weighted least-square estimation that can achieve arbitrary integer-ratio zoom (WLS-AIZ) For a given zooming ratio n, every pixel in a low-resolution (LR) image is associated with an n × n block of high-resolution (HR) pixels in the HR image. In WLS-AIZ, the LR image is interpolated using the bilinear method in advance. Model parameters of every n×n block are worked out through weighted least-square estimation. Subsequently, each pixel in the n × n block is substituted by a combination of its eight neighboring HR pixels using estimated parameters. Finally, a refinement strategy is adopted to obtain the ultimate HR pixel values. The proposed algorithm has significant adaptability to local image structure. Extensive experiments comparing WLS-AIZ with other state of the art image zooming methods demonstrate the superiority of WLS-AIZ. In terms of peak signal to noise ratio (PSNR), structural similarity index (SSIM) and feature similarity index (FSIM), WLS-AIZ produces better results than all other image integer-ratio zoom algorithms.

An adaptive distance-based edge preserving interpolation algorithm for natural images

Abstract: In this paper, we propose a new image interpolation method using adaptive weights based on inverse gradients and distances from the pixels used in prediction. Since the weights are based on different spatial locations of the pixels, this allows preservation of important edge information and hence to prevent extensive blurring across edges in the upsampling process. Experimental results on a large test image set show that the proposed algorithm gives better performance compared to conventional algorithms.