Comparative statistical analysis of the quality of image enhancement techniques

Somvanshi, Shivangi; Kunwar, P.; Tomar, Sewata; Singh, Madhulika

doi:10.1080/19479832.2017.1355336

Cited by 27 publications

(10 citation statements)

References 33 publications

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“…8 and its description in terms of learning and training are as follows. The first layer is a custom convolution operation which is a filter (𝐾) as defined in Equation ( 16) for enhancing fine details in the image [45].…”

Section: Adaptive B-spline Polygon Curve Fitting For Text Localization (Abs-net)mentioning

confidence: 99%

A New Deep Wavefront Based Model for Text Localization in 3D Video

Nandanwar

Shivakumara

Ramachandra

et al. 2022

IEEE Trans. Circuits Syst. Video Technol.

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With the evolution of electronic devices, such as 3D cameras, addressing the challenges of text localization in 3D video (e.g., for indexing) is increasingly drawing the attention of the multimedia and video processing community. Existing methods focus on 2D video and their performance in the presence of the challenges in 3D video, such as shadow areas associated with text and irregularly sized and shaped text, degrades. This paper proposes the first approach that successfully addresses the challenges of 3D video in addition to those of 2D. It employs a number of innovations, among which, the first is the Generalized Gradient Vector Flow (GGVF) for dominant points detection. The second is the Wavefront concept for text candidate point detection from those dominant points. In addition, an Adaptive B-Spline Polygon Curve Network (ABS-Net) is proposed for accurate text localization in 3D videos by constructing tight fitting bounding polygons using text candidate points. Extensive experiments on custom (3D video) and standard datasets (2D video and scene text) show that the proposed method is practical and useful, and overall outperforms existing state-of-the-art methods.

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Section: Adaptive B-spline Polygon Curve Fitting For Text Localization (Abs-net)mentioning

confidence: 99%

A New Deep Wavefront Based Model for Text Localization in 3D Video

Nandanwar

Shivakumara

Ramachandra

et al. 2022

IEEE Trans. Circuits Syst. Video Technol.

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“…Currently, the quality of a fused image can be quantitively evaluated using the metrics [57] structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean squared error (IMMSE), and mean absolute deviation (MAD). The SSIM is an assessment index of the image quality based on computations of luminance, contrast, and structural components of the reference and the reconstructed images, with the overall index a multiplicative combination of these three components.…”

Section: Selection Of Inpainting Quality Metricmentioning

confidence: 99%

Specular Reflection Detection and Inpainting in Transparent Object through MSPLFI

2021

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Multispectral polarimetric light field imagery (MSPLFI) contains significant information about a transparent object’s distribution over spectra, the inherent properties of its surface and its directional movement, as well as intensity, which all together can distinguish its specular reflection. Due to multispectral polarimetric signatures being limited to an object’s properties, specular pixel detection of a transparent object is a difficult task because the object lacks its own texture. In this work, we propose a two-fold approach for determining the specular reflection detection (SRD) and the specular reflection inpainting (SRI) in a transparent object. Firstly, we capture and decode 18 different transparent objects with specularity signatures obtained using a light field (LF) camera. In addition to our image acquisition system, we place different multispectral filters from visible bands and polarimetric filters at different orientations to capture images from multisensory cues containing MSPLFI features. Then, we propose a change detection algorithm for detecting specular reflected pixels from different spectra. A Mahalanobis distance is calculated based on the mean and the covariance of both polarized and unpolarized images of an object in this connection. Secondly, an inpainting algorithm that captures pixel movements among sub-aperture images of the LF is proposed. In this regard, a distance matrix for all the four connected neighboring pixels is computed from the common pixel intensities of each color channel of both the polarized and the unpolarized images. The most correlated pixel pattern is selected for the task of inpainting for each sub-aperture image. This process is repeated for all the sub-aperture images to calculate the final SRI task. The experimental results demonstrate that the proposed two-fold approach significantly improves the accuracy of detection and the quality of inpainting. Furthermore, the proposed approach also improves the SRD metrics (with mean F1-score, G-mean, and accuracy as 0.643, 0.656, and 0.981, respectively) and SRI metrics (with mean structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean squared error (IMMSE), and mean absolute deviation (MAD) as 0.966, 0.735, 0.073, and 0.226, respectively) for all the sub-apertures of the 18 transparent objects in MSPLFI dataset as compared with those obtained from the methods in the literature considered in this paper. Future work will exploit the integration of machine learning for better SRD accuracy and SRI quality.

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“…Since the adoption of satellite-based recording of spectral radiance of ground objects in visible and near-infrared bands became possible, various indices have been developed based on the certain combinations (sum, difference, ratio, linear-additional) of bands (Somvanshi et. al., 2017).…”

Section: Data Analysis Image Processing (Spectral Enhancement)mentioning

confidence: 99%

Unveiling Spatial Variation in Salt Affected Soil of Gautam Buddha Nagar District Based on Remote Sensing Indicators

Somvanshi

Kunwar²,

Vries

et al. 2020

Journal of Landscape Ecology

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Salt accumulation within the soil is one of the subtle ecological issues around the world. An integrated of remote sensing with different statistical techniques has indicated accomplishment for creating soil quality forecasting models. The objective of this research was to unveil the degree and location of the salt affected soils as it has a severe effect on the agricultural crop yield of the Gautam Buddha Nagar (GBN) district. To assess spatial variation of the salt-affected soil a simulation model integrating satellite observation data, artificial neural network (ANN) and multiple linear regression (MLR) was used. The statistical correlation amongst ground-truth data and Landsat original bands and band ratios showed that all the bands and ratios showed a non-significant correlation with SAR. While four optical bands and eleven band ratios showed high correlation with all the soil quality parameters. Combining all the remotely sensed variables into models resulted in the finest fit with the R2 value equal to 0.84, 0.69, 0.59 and 0.85 for EC, pH, ESP and TSS, respectively. The soil quality parameter maps generated using selected models revealed that most of the part of the agricultural land of the study area lies in the range of moderately saline and moderately sodic soil. Further Analytical Hierarchy Process (AHP) was applied to generate overall soil degradation probability map of the district, with respect to salt accumulation. The result revealed that the major portion of the entire agricultural field of the study area lie between low (32.74 %) to moderate (29.53 %) probability zones of salt susceptibility.

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Comparative statistical analysis of the quality of image enhancement techniques

Cited by 27 publications

References 33 publications

A New Deep Wavefront Based Model for Text Localization in 3D Video

A New Deep Wavefront Based Model for Text Localization in 3D Video

Specular Reflection Detection and Inpainting in Transparent Object through MSPLFI

Unveiling Spatial Variation in Salt Affected Soil of Gautam Buddha Nagar District Based on Remote Sensing Indicators

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