Mrinmoy Ghorai

An Image Inpainting Algorithm Using Higher Order Singular Value Decomposition

In this paper, we present an exemplar-based image in painting technique using the higher order singular value decomposition (HOSVD). The two main steps of the proposed method are determination of patch priority and patch completion. Here we adopt gradient-based priority term. For patch completion, we build a stack of the candidate patches corresponding to the target patch. Then we find the coefficients matrix of singular values using HOSVD transformation from the stack and nullify some singular values which corresponds to some artifacts. Next, we invert the HOSVD transform and synthesize the target patch by taking weighted average of filtered candidate patches. We also incorporate local patch consistency in the proposed model. Experimental results show the superiority of the proposed method compared to the competitive methods. The proposed method may be used for restoration of digital images of defective or damaged artifacts.

An image inpainting method using pLSA-based search space estimation

In this paper, we present a novel exemplar-based image inpainting technique based on the local context measure of the target patch. Three main steps of the proposed method are determination of patch priority, the search space estimation for the candidate patches and the patch completion to fill in the unknown pixels of the target patch. In patch priority, we emphasize on the structure by the spatial relationship of neighborhood similar patches and kernel regression based local image structure. We find the search space, sub-regions of the entire source region similar to the region surrounding the target patch, to find the candidate patches. The said search space is estimated using probabilistic latent semantic analysis (pLSA). Last, we infer the unknown pixels of the target patch using pLSA-based context and histogram similarity measure between the target patch and the candidate patches. Experimental results are found to be good compared to the competitive methods and may be used for digital restoration of images of defective or damaged artifacts.

A Fast Video Inpainting Technique

In this paper we present a fast video inpainting technique to infer the unknown information in the target region by maximizing box-based self-similarity and coherence measure. The video inpainting is already proposed in the literature and some of them are able to produce good quality results. However, the bottleneck of those algorithms is they are painfully slow. Here we fill the texture in the target region that preserves the smooth motion of the object without inclusion of any artifacts in reasonable amount of time. Our experiments show that the proposed method is quite efficient to synthesize unknown information in a video and comparable to the existing state-of-the-art methods. Moreover, proposed method is based on box filling and optimization is done on multiple scale using EM algorithm, and is computationally faster than the existing ones.

Image Completion Assisted By Transformation Domain Patch Approximation

In this paper, we propose a novel image completion method using transform domain patch approximation method and kd-tree based nearest neighbor field (NNF) computation in multiscale fashion. In NNF, two important processes are initialization of image target region and candidate patch searching method. Most of the previous techniques choose random initialization with arbitrary source image pixels or garbage values. It may misguide to image completion process and allow to select the bad candidate patches. We solve the problem using higher order singular value decomposition (HOSVD). It smoothly generates information in the target region enhancing the edge sharpness which helps to complete image structure quite successfully. It also preserves texture color in the target region. To overcome the problem of patch searching, we introduce robust kd-tree search method in our patch approximation step. Our experiment and analysis shows that the proposed method can be applied to the various types of image editing tools for natural images.

A Two-Step Image Inpainting Algorithm Using Tensor SVD

In this paper, we present a novel exemplar-based image inpainting algorithm using the higher order singular value decomposition (HOSVD). The proposed method performs inpainting of the target image in two steps. At the first step, the target region is inpainted using HOSVD-based filtering of the candidate patches selected from the source region. It helps to propagate the structure and color smoothly in the target region and restrict to appear unwanted artifacts. But a smoothing effect may be visible in the texture regions due to the filtering. In the second step, we recover the texture by an efficient heuristic approach using the already inpainted image. The experimental results show the superiority of the proposed method compared to the state of the art methods.

Patch sparsity based image inpainting using local patch statistics and steering kernel descriptor

This paper presents a sparse representation based image inpainting method using local patch analysis and geometric structure based feature extraction. In local patch analysis, we approximate the target region by weighted average of some local patches which are frequently occurred within a neighborhood. Local patch statistics is applied to find the most relevant neighbors for each target patch. Further we extract local steering kernel (LSK) based feature to preserve geometric structure and texture sharpness in the target region. The advantage of non local self similarity as redundancy of similar patches in natural images is introduced to find the candidate patches from the whole source region. Based on these local and non local prior information we propose a sparse representation framework for image inpainting. Our proposed method is tested on wide range of natural images. The experimental results show the superiority of the proposed method compared to some of the previous approaches.

An Image Dataset of Bishnupur Terracotta Temples for Digital Heritage Research

Heritage preservation and awareness building has become a major application domain for computer vision techniques in recent days. Bishnupur is an important and well-known heritage site in West Bengal, India. This attractive tourist place is famous for its terracotta temples. In this article, we present an image dataset created by us for developing and evaluating various computer vision algorithms for preservation and visualization of heritage artifacts in digital space. The dataset includes images of some important temples, such as Jor Bangla temple, Kalachand temple, Madan Mohan temple, Nandalal temple, Radha Madhav temple, Rasmancha, and Shyamrai temple. Though this dataset can be used for many types of computer vision and image analysis algorithms, we have shown here its usefulness by testing the images for four different applications: 3D reconstruction, image inpainting, texture classification, and content-specific figure spotting and retrieval. Note that we have shown the results of baseline methods only. The dataset is publicly available at http://www.isical.ac.in/~bsnpr/ for research purpose only.

A Group-Based Image Inpainting Using Patch Refinement in MRF Framework

This paper presents a Markov random field (MRF)-based image inpainting algorithm using patch selection from groups of similar patches and optimal patch assignment through joint patch refinement. In patch selection, a novel group formation strategy based on subspace clustering is introduced to search the candidate patches in relevant source region only. This improves patch searching in terms of both quality and time. We also propose an efficient patch refinement scheme using higher order singular value decomposition to capture underlying pattern among the candidate patches. This eliminates random variation and unwanted artifacts as well. Finally, a weight term is computed, based on the refined patches and is incorporated in the objective function of the MRF model to improve the optimal patch assignment. Experimental results on a large number of natural images and comparison with well-known existing methods demonstrate the efficacy and superiority of the proposed method.

A Patch-Based Constrained Inpainting for Damaged Mural Images

Heritage artefacts and monuments are important components of social science. Those are under constant threat of decaying and degrading due to exposition to unfriendly natural environment and hooliganism. Restoration of heritage artefacts such as murals and paintings is an important task for preservation of social, cultural and political history of a nation. As being in the temples in India, a significant share of murals and paintings are not accessible for physical restoration. This motivates many researchers to put effort in restoration of such priceless paintings and reliefs digitally in augmented reality domain. In this work, we have proposed an exemplar based coherent texture synthesis technique to inpaint the digital image of damaged portion of murals and paintings. Inpainting method, while maintaining the spatial coherency, usually introduces blurring as well as structured noise to the inpainted regions. To overcome this problem, we have combined the proposed patch-based diffusion technique with a novel technique for high-frequency generation that leads to edge sharpening and denoising simultaneously. Finally, the proposed constraint and interactive nature of the method is found efficient to handle rich variety of such paintings. The experimental results with empirical evaluation show the efficacy of the proposed method.

Scale-Invariant Image Inpainting Using Gradient-Based Image Composition

In this paper, we propose a novel scale-invariant image inpainting algorithm that combines several inpainted images obtained from multiple pyramids of different coarsest scales. To achieve this, first we build the pyramids and then we run an image inpainting algorithm individually on each of the pyramids to obtain different inpainted images. Finally, we combine those inpainted images by a gradient based approach to obtain the final inpainted image. The motivation of this approach is to solve the problem of appearing artifacts in traditional single pyramid-based approach since the results depend on the starting scale of the pyramid. Here we assume that most of the inpainted images produced by the pyramids are quite good. However, some of them may have artifacts and these artifacts are eliminated by gradient based image composition. We test the proposed algorithm on a large number of natural images and compare the results with some of the existing methods to demonstrate the efficacy and superiority of the proposed method.