Faculty Publications
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Publications by NITK Faculty
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Item Automatic method for contrast enhancement of natural color images(Korean Institute of Electrical Engineers, 2015) Shyam, L.; Narasimhadhan, A.V.The contrast enhancement is great challenge in the image processing when images are suffering from poor contrast problem. Therefore, in order to overcome this problem an automatic method is proposed for contrast enhancement of natural color images. The proposed method consist of two stages: in first stage lightness component in YIQ color space is normalized by sigmoid function after the adaptive histogram equalization is applied on Y component and in second stage automatic color contrast enhancement algorithm is applied on output of the first stage. The proposed algorithm is tested on different NASA color images, hyperspectral color images and other types of natural color images. The performance of proposed algorithm is evaluated and compared with the other existing contrast enhancement algorithms in terms of colorfulness metric and color enhancement factor. The higher values of colorfulness metric and color enhancement factor imply that the visual quality of the enhanced image is good. Simulation results demonstrate that proposed algorithm provides higher values of colorfulness metric and color enhancement factor as compared to other existing contrast enhancement algorithms. The proposed algorithm also provides better visual enhancement results as compared with the other existing contrast enhancement algorithms. © The Korean Institute of Electrical Engineers.Item A Novel Adaptive Cuckoo Search Algorithm for Contrast Enhancement of Satellite Images(Institute of Electrical and Electronics Engineers, 2017) Suresh, S.; Lal, S.; Chintala, C.S.; Kiran, M.S.Owing to the increased demand for satellite images for various practical applications, the use of proper enhancement methods are inevitable. Visual enhancement of such images mainly focuses on improving the contrast of the scene procured, conserving its naturalness with minimum image artifacts. Last one decade traced an extensive use of metaheuristic approaches for automatic image enhancement processes. In this paper, a robust and novel adaptive Cuckoo search based Enhancement algorithm is proposed for the enhancement of various satellite images. The proposed algorithm includes a chaotic initialization phase, an adaptive Levy flight strategy and a mutative randomization phase. Performance evaluation is done by quantitative and qualitative results comparison of the proposed algorithm with other state-of-the-art metaheuristic algorithms. Box-and-whisker plots are also included for evaluating the stability and convergence capability of all the algorithms tested. Test results substantiate the efficiency and robustness of the proposed algorithm in enhancing a wide range of satellite images. © 2008-2012 IEEE.Item Visibility enhancement of images degraded by hazy weather conditions using modified non-local approach(Elsevier GmbH journals@elsevier.com, 2018) Pal, N.S.; Lal, S.; Shinghal, K.Natural images captured under bad weather situations suffer from poor visibility and contrast problems. Object tracking and recognition under hazy bad weather conditions is a very difficult task for real time applications. Therefore, in this paper, we have proposed an efficient dehazy algorithm for visibility and contrast enhancement of color hazy images. The proposed algorithm works in two phases. In the first phase, a non local approach is applied in the hazy model, which is a pixel based approach not a patch based. Pixels are spread over the entire image plane and positioned at different distance from the sensor, so this approach is called non local. Degradation is different for every pixel therefore; estimation of the transmission map for every pixel through the haze line is the essential step. After the first phase, the image becomes unnatural and dimmed, therefore to proper tone mapping and improving the visual quality of the image, we applied the S-shaped mapping function in the second phase. The quantitative results of the proposed algorithm and other existing dehazy algorithms for color hazy images are obtained in terms of Hazy Reduction factor(HRF), and measure of enhancement factor(EMF) on different hazy image databases. Qualitative results reveal that the visual quality of the proposed algorithm is better than other existing de-hazy algorithms. Simulation results demonstrate that the proposed algorithm provides better results as compared to other existing dehazy algorithms for color hazy images. Proposed algorithm is highly efficient as compare to other latest dehazy algorithms. © 2018 Elsevier GmbHItem A robust framework for quality enhancement of aerial remote sensing images(Elsevier B.V., 2018) Karuna Kumari, E.; Das, D.; Suresh, S.; Lal, S.; Narasimhadhan, A.V.This paper proposes a robust framework for quality restoration of remotely sensed aerial images. Proposed framework works in three steps: (1) Efficient color balancing and saturation adjustment, (2) Efficient color restoration, (3) Modified contrast enhancement using particle swarm optimization (PSO). In order to show the robustness, step-wise results of proposed framework is illustrated. Several aerial images from two publically available datasets are tested to support the robustness of the proposed framework over existing image quality restoration methods. The experimental results of proposed framework and other existing quality restoration methods are compared in terms of NIQMC, BIQME, MICHELSON, DE, EME and PIXDIST along with visual experimental results. Based on experimental results conducted on several aerial images suggest that the proposed framework is outperform over existing quality restoration methods. © 2018 Elsevier B.V.Item A Retinex-Based Variational Model for Enhancement and Restoration of Low-Contrast Remote-Sensed Images Corrupted by Shot Noise(Institute of Electrical and Electronics Engineers, 2020) Febin, I.P.; Padikkal, P.; Bini, A.A.Remotely sensed images are widely used in many imaging applications. Images captured under adverse atmospheric conditions lead to degraded images that are contrast deficient and noisy. This study is intended to address these defects of remotely sensed data efficiently. A perceptually inspired variational model is designed based upon the Bayesian framework, powered by the retinex theory. The atmospheric noise or the shot noise (precisely following a Poisson distribution) and contrast inhomogeneity are addressed in this article. The model thus designed is tested and verified both visually and quantitatively using various test data under different statistical measures. The comparative study reveals the efficiency of the model. © 2020 IEEE.Item Accelerating randomized image secret sharing with GPU: contrast enhancement and secure reconstruction using progressive and convolutional approaches(Springer, 2024) Holla, M.; Suma, D.; Pais, A.R.Image Secret Sharing (ISS) is a cryptographic technique used to distribute secret images among multiple users. However, current Visual Secret Sharing (VSS) schemes produce a halftone image with only 50% contrast when reconstructing the original image. To overcome this limitation, the Randomized Image Secret Sharing (RISS) scheme was introduced. RISS achieves a higher contrast of 70% when extracting the secret image but comes with a high computational cost. This research paper presents a novel approach called Graphics Processing Unit (GPU)-based Randomized Image Secret Sharing (GRISS), which utilizes data parallelism within the RISS pipeline. The proposed technique also incorporates an Autoencoder-based Single Image Super-Resolution (ASISR) to enhance the contrast of the recovered image. The performance of GRISS is evaluated against RISS, and the contrast of the ASISR images is compared to current benchmark models. The results demonstrate that GRISS outperforms state-of-the-art models in both efficiency and effectiveness. © The Author(s) 2024.