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Publication Metadata only Design of Complex Adaptive Multiresolution Directional Filter Bank and Application to Pansharpening(Springer, 01-02-2017) Gajbhar, Shrishail S; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; DA-IICT, Gandhinagar; Gajbhar, Shrishail S (201121016)This paper proposes a new 2-D transform design, namely�complex adaptive multiresolution directional filter bank, to represent the spatial orientation features of an input image�adaptively. The proposed design is completely�shift invariant�and represents the input image by one low-pass and multiscale�N�directional band-pass subbands. Here,�N�represents estimated number of dominant directions present in the input image. Our design consists of two main filter bank stages. A fix partitioned complex-valued directional filter bank (CDFB) is at the core of the design followed by a novel partition filter bank stage. Fine partitioning of the CDFB subbands is used to get the adaptive nature of the proposed transform. The partitioning decision is made based on the directional significance of range of CDFB subband angle selectivity in the input image. Partition filter bank stage which�nonuniformly�partitions the CDFB subbands provides total�N�dominant direction selective subbands. Local orientation map of the input image is used to determine the dominant directions and hence�N. For better sparsity properties, we design the multiresolution stage with filters having high�vanishing moments�and better frequency selectivity. Applicability of the proposed adaptive design is shown for pansharpening of multispectral images. Our proposed pansharpening approach is evaluated on images captured using QuickBird and IKONOS-2 satellites. Results obtained using the proposed approach on these datasets show considerable improvements in qualitative as well as quantitative evaluations when compared to state-of-the-art pansharpening approaches including transform-based methods.Publication Metadata only Abundance estimation using discontinuity preserving and sparsity-induced priors(IEEE, 30-05-2019) Patel, Jignesh R; Joshi, Manjunath V; Bhatt, Jignesh S; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; DA-IICT, Gandhinagar; Patel, Jignesh R (201521011)Abundance estimation is used to infer the proportions of endmembers with the given endmember signatures and reflectance value at each location. In this paper, we propose a two-phase iterative approach to estimate the abundances (fractions) of materials (endmembers) from the pixels of hyperspectral images (HSIs) by using the energy minimization framework. A linear mixture model is used to define the data term. We observe that abundance maps have homogeneous regions with limited discontinuity, and they exhibit spatial redundancy. Hence, we use inhomogeneous Gaussian Markov random field (IGMRF) and sparsity-induced priors as the regularization terms. While the IGMRF prior captures the smoothness and preserves discontinuities among abundance values, the sparsity-induced prior accounts for redundancy. We calculate the IGMRF parameters at every pixel location and learn a dictionary and the sparse representation for abundances using the initial estimate in phase 1, while the final abundance maps are estimated in phase 2. In order to learn the sparsity, we use the approach based on K-singular value decomposition. Both the IGMRF and sparseness parameters are initialized using an initial estimate of abundances and refined using the two-phase iterative approach. The experiments are conducted on synthetic hyperspectral HSIs with different noise levels, as well as on two real HSIs. The results are qualitatively and quantitatively compared with state-of-the-art approaches. Experimental results demonstrate the effectiveness of the proposed approach.Publication Metadata only SNR wall for generalized energy detector in the presence of noise uncertainty and fading(Elsevier, 01-02-2019) Captain, Kamal; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; DA-IICT, Gandhinagar; Captain, Kamal (201321003)The performance of energy detection (ED) degrades under low�SNR, noise uncertainty (NU) and fading. The generalized energy detector (GED) is obtained by changing the squaring operation in ED by an arbitrary positive number�. In this paper, we investigate the signal to noise ratio (SNR) wall for GED under diversity considering NU as well as fading by considering�-Law combining (pLC) and�law selection (pLS) diversity. First, the�SNR�walls considering�AWGN�channel are derived. It is shown that for pLC diversity, increasing��results in lower�SNR�wall. It is also shown that under no diversity and pLS diversity, the�SNR�wall is independent of�. The analysis is then extended to the channel with Nakagami fading where it is shown that the SNR wall increases significantly. As a byproduct of this work, we also study the effect of NU and fading on the detection performance and show that above certain value, the effect of NU is more severe when compared to the fading. The effect of��on the performance is analyzed and it is shown that the performance is the best for values of��close to 2. The performance of pLC and pLS is also compared.Publication Metadata only A regularized pan-sharpening approach based on self-similarity and Gabor prior(Taylor & Francis, 01-01-2017) Upla, Kishor P; Joshi, Manjunath V; Khatri, Nilay; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; DA-IICT, Gandhinagar; Khatri, NilayIn this article, we propose a new regularization-based approach for pan-sharpening based on the concepts of self-similarity and Gabor prior. The given low spatial resolution (LR) and high spectral resolution multi-spectral (MS) image is modelled as degraded and noisy version of the unknown high spatial resolution (HR) version. Since this problem is ill-posed, we use regularization to obtain the final solution. In the proposed method, we first obtain an initial HR approximation of the unknown pan-sharpened image using self-similarity and sparse representation (SR) theory. Using self-similarity, we obtain the HR patches from the given LR observation by searching for matching patches in its coarser resolution, thereby obtaining LR�HR pairs. An SR framework is used to obtain the patch pairs for which no matches are available for the patches in LR observation. The entire set of matched HR patches constitutes initial HR approximation (initial estimate) to the final pan-sharpened image which is used to estimate the degradation matrix as used in our model. A regularization framework is then used to obtain the final solution in which we propose to use a new prior which we refer as Gabor prior that extracts the bandpass details from the registered panchromatic (Pan) image. In addition, we also include Markov random field (MRF) smoothness prior that preserves the smoothness in the final pan-sharpened image. MRF parameter is derived using the initial estimate image. The final cost function consists of data fitting term and two prior terms corresponding to Gabor and MRF. Since the derived cost function is convex, simple gradient-based method is used to obtain the final solution. The efficacy of the proposed method is evaluated by conducting the experiments on degraded as well as on un-degraded datasets of three different satellites, i.e., Ikonos-2, Quickbird, and Worldview-2. The results are compared on the basis of traditional measures as well as recently proposed quality with no reference (QNR) measure, which does not require the reference image.Publication Metadata only Auto-inpainting Heritage Scenes: a Complete Framework for Detecting and Infilling Cracks in Images and Videos with Quantitative Assessment(Springer, 01-04-2015) Padalkar, Milind G; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; DA-IICT, Gandhinagar; Padalkar, Milind G (201121015)The need for preservation of cultural heritage has necessitated the research on digitally repairing the photographs of damaged monuments. In this paper, we first propose a technique for automatically detecting the cracked regions in photographs of monuments. Unlike the usual practice of manually selecting the mask for inpainting, the detected regions are supplied to an inpainting algorithm. Thus, the process of digitally repairing the cracked regions that physical objects have, using inpainting, is completely automated. The detection of cracked regions is based on comparison of patches, for which we use a measure derived from the edit distance, which is a popular string metric used in the area of text mining. Further, we extend this method to perform inpainting of video frames by making use of the scale-invariant feature transform and homography. We consider the camera to move while capturing video of the heritage site, as such videos are typically captured by novices, hobbyists and tourists. Finally, we also propose a video quality measure to quantify the temporal consistency of the inpainted video. Experiments have been carried out on videos captured from the heritage site at Hampi, India.Publication Metadata only Multiresolution Image Fusion: Use of Compressive Sensing and Graph Cuts(IEEE, 01-05-2014) Harikumar, V; Gajjar, Prakash P; Joshi, Manjunath V; Raval, Mehul S; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; DA-IICT, Gandhinagar; Harikumar, V (201011016); Gajjar, Prakash P (200521004)In this paper, we propose a new approach for multiresolution fusion, i.e., obtaining a high spatial and spectral resolution multispectral (MS) image using the available low spatial resolution MS and the high spatial resolution Panchromatic (Pan) image. Our approach is based on the idea of compressive sensing (CS) and graph cuts. Assuming that both the MS and Pan images have the same sparseness, a close approximation to the MS image is obtained from the Pan image using the theory of compressive sensing and l1 minimization. We then use regularization framework to obtain fused image. The low resolution (LR) MS image is modeled as degraded and noisy version of fused image in which degradation matrix entires estimated using the close approximation are used. The regularization is carried out by using truncated quadratic smoothness prior which takes care of preservation of the discontinuities in the fused image. A suitable energy function is then formed consisting of data fitting term and prior term. Minimization of the energy function is carried out using a computationally efficient graph cuts optimization to obtain final fused image. Advantage of our approach is that the Pan and MS images need not be registered. This is because, we are not directly using the Pan digital numbers to derive the fused image. The effectiveness of the proposed method is illustrated by conducting experiments on real satellite images. Subjective and quantitative comparison of the proposed method with the state-of-the-art approaches indicates efficacy of our approach.Publication Metadata only Multiresolution image fusion using edge-preserving filters(SPIE, 01-07-2015) Upla, Kishor P; Joshi, Sharad; Joshi, Manjunath V; Gajjar, Prakash P; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; DA-IICT, GandhinagarWe propose two approaches of multiresolution image fusion using multistage guided filter and difference of Gaussians (DoGs). In a multiresolution image fusion problem, the given multispectral (MS) and panchromatic (Pan) images have high spectral and high spatial resolutions, respectively. One can obtain the fused image using these two images by injecting the missing high frequency details from the Pan image into the MS image. The quality of the final fused image will then depend on the method used for high frequency details extraction and also on the technique for injecting these details into the MS image. Specifically, we have chosen the guided filter and DoGs for detail extraction since these are more versatile in applications involving feature extraction, denoising, and so on. The detail extraction process in the fusion approach using a guided filter exploits the relationship between the Pan and MS images by utilizing one of them as a guidance image while extracting details from the other. The final fused image is obtained by adding the extracted high frequency details to the corresponding MS image. This way, the spatial distortion of the MS image is reduced by consistently combining the details obtained using both MS and Pan images. In the fusion method using DoGs, the high frequency details are extracted in the first and second levels by subtracting the blurred images of the original Pan. The extracted details at both DoGs are added to the MS image to obtain the final fused image. Advantages and disadvantages of each method are discussed and the comparison of the results is shown between the two. The results are also compared with the traditional and the state-of-the-art methods using the images captured using different satellites such as Quickbird, Ikonos-2, and Worldview-2. The quantitative assessment is evaluated using the conventional measures as well as using a relatively new index, i.e., quality with no reference which does not require a reference image. The results and measures clearly show that there is promising improvement in the quality of the fused image using the proposed approaches.Publication Metadata only An Edge Preserving Multiresolution Fusion: Use of Contourlet Transform and MRF Prior(IEEE, 01-06-2015) Upla, Kishor P; Joshi, Manjunath V; Gajjar, Prakash P; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; DA-IICT, GandhinagarIn this paper, we propose a new approach for multiresolution fusion using contourlet transform (CT). The method is based on modeling the low spatial resolution (LR) and high spectral resolution multispectral (MS) image as the degraded and noisy version of their high spatial resolution version. Since this is an ill-posed problem, it requires regularization in order to obtain the final solution. In this paper, we first obtain the initial estimate of the fused image from the available MS image and the panchromatic (Pan) image by using the CT domain learning. Since CT provides better directional edges, the initial estimate has better edge details. Using the initial estimate, we obtain the degradation that accounts for the aliasing between the LR MS image and fused image. Regularization is carried out by modeling the texture of the final fused image as a homogeneous Markov random field (MRF) prior, where the MRF parameter is estimated using the initial estimate. The use of MRF prior on the final fused image takes care of the spatial dependencies among the pixels. A simple gradient-based optimization technique is used to obtain the final fused image. Although we use homogeneous MRF, the proposed approach preserves the edges in the final fused image by retaining the edges from the initial estimate and by carrying out the optimization on nonedge pixels only. Therefore, the advantage of the proposed method lies in preserving the discontinuities without using the discontinuity preserving prior, thus avoiding the use of computationally taxing optimization techniques for regularization purposes. In addition, the proposed method causes minimum spectral distortion since it learns the texture using contourlet coefficients and does not use actual Pan image pixel intensities. We demonstrate the effectiveness of our approach by conducting the experiments using subsampled and nonsubsampled CT on different data sets captured using Ikonos-2, Quickbird, and Worldview-2 satellites.Publication Metadata only Super-resolution of hyperspectral images: Use of optimum wavelet filter coefficients and sparsity regularization(IEEE, 01-04-2015) Patel, Rakesh C; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; DA-IICT, Gandhinagar; Patel, Rakesh C (200921003)Hyperspectral images (HSIs) have high spectral resolution, but they suffer from low spatial resolution. In this paper, a new learning-based approach for super-resolution (SR) using the discrete wavelet transform (DWT) is proposed. The novelty of our approach lies in designing application-specific wavelet basis (filter coefficients). An initial estimate of SR is obtained by using these filter coefficients while learning the high-frequency details in the wavelet domain. The final solution is obtained using a sparsity-based regularization framework, in which image degradation and the sparseness of SR are estimated using the estimated wavelet filter coefficients (EWFCs) and the initial SR estimate, respectively. The advantage of the proposed algorithm lies in 1) the use of EWFCs to represent an optimal point spread function to model image acquisition process; 2) use of sparsity prior to preserve neighborhood dependencies in SR image; and 3) avoiding the use of registered images while learning the initial estimate. Experiments are conducted on three different kinds of images. Visual and quantitative comparisons confirm the effectiveness of the proposed method.Publication Metadata only A data-driven stochastic approach for unmixing hyperspectral imagery(IEEE, 01-06-2014) Bhatt, Jignesh S; Joshi, Manjunath V; Raval, Mehul S; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; DA-IICT, Gandhinagar; Bhatt, Jignesh S (200921006)In this paper, we propose a two-step Bayesian approach to handle the ill-posed nature of the unmixing problem for accurately estimating the abundances. The abundances are dependent on the scene contents and they represent mixing proportions of the endmembers over an area. In this work, a linear mixing model (LMM) is used for the image formation process in order to derive the data term. In the first step, a Huber-Markov random field (HMRF)-based prior distribution is assumed to model the dependencies within the abundances across the spectral space of the data. The threshold used in the HMRF prior is derived from an initial estimate of abundances obtained using the matched filters. This makes the HMRF prior data-driven, i.e., dHMRF. Final abundance maps are obtained in the second step within a maximum a posteriori probability (MAP) framework, and the objective function is optimized using the particle swarm optimization (PSO). Theoretical analysis is carried out to show the effectiveness of the proposed method. The approach is evaluated using the synthetic and real AVIRIS Cuprite data. The proposed method has the following advantages. 1) The estimated abundances are resistant to noise since they are based on an initial estimate that has high signal-to-noise ratio (SNR). 2) The variance in the abundance maps is well preserved since the threshold in the dHMRF is derived from the data.