Srimanta Mandal

Edge preserving single image super resolution in sparse environment

Quality of an image is associated with edge of the image. It is important to preserve the edge of the image while deriving high resolution (HR) image from low resolution (LR) image, also known as superresolution (SR) problem. This paper proposes an edge preserving constraint, which preserve the edge information of image by minimizing the differences between edges of LR image and the edges of the reconstructed image (down-sampled version), in sparse coding based SR problem. Partial edge evidences, derived using 1-D processing of image, are used separately in the constraints. The experimental results show that proposed approach preserves the edges of image as well as outperforms objectively the existing SR approaches.

Noise adaptive super-resolution from single image via non-local mean and sparse representation

Abstract Super-resolution from a single image is a challenging task, more so, in presence of noise with unknown strength. We propose a robust super-resolution algorithm which adapts itself based on the noise-level in the image. We observe that dependency among the gradient values of relatively smoother patches diminishes with increasing strength of noise. Such a dependency is quantified using the ratio of first two singular values computed from local image gradients. The ratio is inversely proportional to the strength of noise. The number of patches with smaller ratio increases with increasing strength of noise. This behavior is used to formulate some parameters that are used in two ways in a sparse-representation based super-resolution approach: i) in computing an adaptive threshold, used in estimating the sparse coefficient vector via the iterative thresholding algorithm, ii) in choosing between the components representing image details and non-local means of similar patches. Furthermore, our approach constructs dictionaries by coarse-to-fine processing of the input image, and hence does not require any external training images. Additionally, an edge preserving constraint helps in better edge retention. As compared to state-of-the-art approaches, our method demonstrates better efficacy for optical and range images under different types and strengths of noise.

Hierarchical example-based range-image super-resolution with edge-preservation

We propose an example-based approach for enhancing resolution of range-images. Unlike most existing methods on range-image superresolution (SR), we do not employ a colour image counterpart for the range-image. Moreover, we use only a small set of range-images to construct a dictionary of exemplars. Considering the importance of edges in range-image SR, our formulation involves an edge-based constraint to better weight appropriate patches from the dictionary in a sparse-representation framework. Moreover, realizing the need for large up-sampling factors in case of range-images, we follow a hierarchical strategy for estimating the high-resolution range-images. We demonstrate that our strategy yields considerable improvements over the state-of-the-art approaches for range-image SR.

Employing structural and statistical information to learn dictionary(s) for single image super-resolution in sparse domain

Abstract It has been argued that structural information plays a significant role in the perceptual quality of images, but the importance of statistical information cannot be neglected. In this work, we have proposed an approach, which explores both structural and statistical information of image patches to learn multiple dictionaries for super-resolving an image in sparse domain. Structural information is estimated using dominant edge orientation, and mean value of the intensity levels of an image patch is used to represent statistical information. During reconstruction, a low resolution test patch is inspected for its structural as well as statistical information to choose a suitable dictionary. This helps in preserving the orientation of edge during super-resolution process. Results are further improved by adding an edge (magnitude of edge) preserving constraint, which maintains the edge continuity of super-resolved image with the input low resolution image. Thus, both characteristics of edge, i.e., orientation and magnitude are preserved in our proposed approach. The experimental results demonstrate the usefulness of the proposed approach in comparison to state-of-the-art approaches.

Super-resolving a Single Intensity/Range Image via Non-local Means and Sparse Representation

We propose an example-based super-resolution (SR) framework, which uses a single input image and, unlike most of the SR methods does not need an external high resolution (HR) dataset. Our SR approach is based in sparse representation framework, which depends on a dictionary, learned from the given test image across different scales. In addition, our sparse coding focuses on the detail information of the image patches. Furthermore, in the above process we have considered non-local combination of similar patches in the input image, which assist us to improve the quality of the SR result. We demonstrate the effectiveness of our approach for intensity images as well as range images. Contemplating the importance of edges in images of both these modalities, we have added an edge preserving constraint that will maintain the continuity of edge related information to the input low resolution image. We investigate the performance of our approach by rigorous experimental analysis and it shows to perform better than some state-of-the-art SR approaches.

Depth Map Restoration From Undersampled Data

Depth map sensed by low-cost active sensor is often limited in resolution, whereas depth information achieved from structure from motion or sparse depth scanning techniques may result in a sparse point cloud. Achieving a high-resolution (HR) depth map from a low resolution (LR) depth map or densely reconstructing a sparse non-uniformly sampled depth map are fundamentally similar problems with different types of upsampling requirements. The first problem involves upsampling in a uniform grid, whereas the second type of problem requires an upsampling in a non-uniform grid. In this paper, we propose a new approach to address such issues in a unified framework, based on sparse representation. Unlike, most of the approaches of depth map restoration, our approach does not require an HR intensity image. Based on example depth maps, sub-dictionaries of exemplars are constructed, and are used to restore HR/dense depth map. In the case of uniform upsampling of LR depth map, an edge preserving constraint is used for preserving the discontinuity present in the depth map, and a pyramidal reconstruction strategy is applied in order to deal with higher upsampling factors. For upsampling of non-uniformly sampled sparse depth map, we compute the missing information in local patches from that from similar exemplars. Furthermore, we also suggest an alternative method of reconstructing dense depth map from very sparse non-uniformly sampled depth data by sequential cascading of uniform and non-uniform upsampling techniques. We provide a variety of qualitative and quantitative results to demonstrate the efficacy of our approach for depth map restoration.

Multi-scale image denoising while preserving edges in sparse domain

Image denoising is a classical and fundamental problem in image processing community. An important challenge in image denoising is to preserve image details while removing noise. However, most of the approaches depend on smoothness assumption of natural images to produce results with smeared edges, hence, degrading the quality. To address this concern, we propose two constraints to better preserve the edges while denoising the image via the sparse representation framework. The first constraint attempts to preserve the edges at the coarser scales of the image as the level of noise drop dramatically at coarser scales. Different levels of scales are considered to account different strength of noise. The second constraint prevents transitional smoothing by preserving the edges of intermediate image estimates across iterations. Experimental results demonstrate the ability of the proposed approach in removing noise while preserving edges in comparison to the state-of-the art approaches.

Significance of dictionary for sparse coding based pose invariant face recognition

This paper deals with the dictionary for sparse coding based pose invariant face recognition. A two stage face recognition system is proposed in sparse coding frame work. The first stage is designed to recognize the pose of an incoming test image. It is proposed to perform pose classification as a three class classification problem in sparse coding frame work, each class representing images for frontal, left side or right side pose. This is done to choose the appropriate Weighted Decomposition Face (WD Face) dictionary, which is found to be suitable for sparse coding based face recognition. The second stage obtains the identity of the person with the help of pose specific WD Face dictionary chosen in the first stage. Experimental results have shown that the proposed approach is a simple yet robust sparse coding based face recognition system which is invariant of pose and illumination.

Single Noisy Image Super Resolution by Minimizing Nuclear Norm in Virtual Sparse Domain

Super-resolving a noisy image is a challenging problem, and needs special care as compared to the conventional super resolution approaches, when the power of noise is unknown. In this scenario, we propose an approach to super-resolve single noisy image by minimizing nuclear norm in a virtual sparse domain that tunes with the power of noise via parameter learning. The approach minimizes nuclear norm to explore the inherent low-rank structure of visual data, and is further augmented with coarse-to-fine information by adaptively re-aligning the data along the principal components of a dictionary in virtual sparse domain. The experimental results demonstrate the robustness of our approach across different powers of noise.

Patch Similarity in Transform Domain for Intensity/Range Image Denoising with Edge Preservation.

For the image denoising task, the prior information obtained from grouping similar non-local patches has been shown to serve as an effective regularizer. Nevertheless, noise may create ambiguity in grouping similar patches, hence it may degrade the results. However, most of the non-local similarity based approaches do not take care of the issue of noisy grouping. Hence, we propose to denoise an image by mitigating the issue of grouping non-local similar patches in presence of noise in transform domain using sparsity and edge preserving constraints. The effectiveness of the transform domain grouping of patches is utilized for learning dictionaries, and is further extended for achieving an initial approximation of sparse coefficient vector for the clean image patches. We have demonstrated the results of effective grouping of similar patches in denoising intensity as well as range images.