scispace - formally typeset
Search or ask a question
Author

Vijayan Ellappan

Bio: Vijayan Ellappan is an academic researcher from VIT University. The author has contributed to research in topics: Synchronization (computer science) & Sparse approximation. The author has an hindex of 2, co-authored 5 publications receiving 7 citations.

Papers
More filters
Journal ArticleDOI
01 Nov 2017
TL;DR: This work uses sparse representation to identify the blur kernel using radon transformation and Fourier for the length calculation of the image and uses Lucy Richardson algorithm which is also called NON-Blind(NBID) Algorithm for more clean and less noisy image output.
Abstract: Blur is a common in so many digital images. Blur can be caused by motion of the camera and scene object. In this work we proposed a new method for deblurring images. This work uses sparse representation to identify the blur kernel. By analyzing the image coordinates Using coarse and fine, we fetch the kernel based image coordinates and according to that observation we get the motion angle of the shaken or blurred image. Then we calculate the length of the motion kernel using radon transformation and Fourier for the length calculation of the image and we use Lucy Richardson algorithm which is also called NON-Blind(NBID) Algorithm for more clean and less noisy image output. All these operation will be performed in MATLAB IDE.

8 citations

Journal ArticleDOI
01 Nov 2017
TL;DR: This concept uses computational images that are to be compared with original images of the street taken to determine the traffic level percentage and set the timing for the traffic signal accordingly which are used to reduce the traffic stoppage on traffic lights.
Abstract: This paper shows the work on traffic analysis and control till date. It shows an approach to regulate traffic the use of image processing and MATLAB systems. This concept uses computational images that are to be compared with original images of the street taken in order to determine the traffic level percentage and set the timing for the traffic signal accordingly which are used to reduce the traffic stoppage on traffic lights. They concept proposes to solve real life scenarios in the streets, thus enriching the traffic lights by adding image receivers like HD cameras and image processors. The input is then imported into MATLAB to be used. as a method for calculating the traffic on roads. Their results would be computed in order to adjust the traffic light timings on a particular street, and also with respect to other similar proposals but with the added value of solving a real, big instance.

3 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: The results largely present MATLAB as a veritable approach for image processing operations as well as providing an empirical-based method using two-dimensional discrete cosine transform (2D-DCT) derived from its coefficients.
Abstract: Owing to recent technological advancement, computers and other devices such as phones and digital cameras running several image editing software applications can be further exploited for other operations such as digital image processing operations. This paper attempts to conduct performance evaluation of the various image processing techniques using MATLAB-based analytics. Compared to the conventional techniques and other state-of-the-art applications used for image processing, MATLAB gives several advantages. MATLAB-based technique provides easy implementation and testing of algorithms without recompilation, and provides easy debugging with extensive data analysis and visualization. Besides, MATLAB's computational codes can be enhanced and exploited to process and create simulations of both still and video images. In addition, MATLAB codes are much concise compared to c++, thus making it easier for perusing and troubleshooting. MATLAB can handle errors prior to execution by proposing various ways to make the code faster. The proposed technique enables advanced image processing operations such as image cropping/resizing, image denoising, blur removal, and image sharpening. The study aims at providing readers with the most recent image processing application-tools running on MATLAB platform. We also provide an empirical-based method of image processing using two-dimensional discrete cosine transform (2D-DCT) derived from its coefficients. With the different and most recent algorithms running on MATLAB toolbox, we provide simulations of several images to evaluate the performance of our proposed technique. The simulation results largely present MATLAB as a veritable approach for image processing operations.

8 citations

Journal ArticleDOI
01 Oct 2020
TL;DR: In this article, an image descriptor based on Gaussian mixture model in auto-encoder (GMM-AE) was used as a primary layer in convolutional neural networks.
Abstract: In this work, deep learning for enhancing the sharpness of blurred image is investigated. Initial pre-processing is blur image kernel estimation which is critical for blind image de-blurring. In prior investigation, handcrafted blur features are optimized for certain uniform blur, which is unrealistic for blind de-convolution. To deal with this crisis, initially this work attempts to carry out kernel matrix estimation using latent semantic analysis (KME-LSA) in dermatology image. In order to enhance the image sparseness, this work modelled an image descriptor based on Gaussian mixture model in auto-encoder (GMM-AE) as a primary layer in convolutional neural networks. The functionality of the proposed GMM-AE triggers the selection of efficient features for subsequent layers in CNN. The features extracted from the integrated trained GMM-AE in CNN can fine-tune the quality of blurred image. Datasets used are melanoma-based dermascope images. Pre-processing procedures are carried out by LSA-based kernel matrix estimation. The attained sharp image outcome is given to the proposed model for effective feature extraction and to attain improved blind image. The anticipated KME-LSA and GMM-AE in CNN estimates blur parameters with high accuracy. Experiment illustrates the efficacy of proposed method and the competitive outcomes are compared with state-of-the-art datasets. Simulation was carried out in MATLAB environment; performance metrics like MSE—227.6, PSNR—33.6762, SSIM—0.9755 and VIF—0.08162 are evaluated. The results show better trade-off than the prevailing techniques.

6 citations

Journal ArticleDOI
TL;DR: Comprehensive experimental results show that the proposed dynamic structure prior (DSP) outperforms previous methods in both commonly used datasets with various noise levels and real world images, from monochrome image to color image.

3 citations

Journal ArticleDOI
TL;DR: Restoration noisy blurred images by guided filter and inverse filtering can be used for enhancing images from different types of degradation was proposed and illustrated good outcomes compared with other methods for removing noise and blur based on PSNR measure.
Abstract: The development of complex life leads into a need using images in several fields, because these images degraded during capturing the image from mobiles, cameras and persons who do not have sufficient experience in capturing images. It was important using techniques differently to improve images and human perception as image enhancement and image restoration etc. In this paper, restoration noisy blurred images by guided filter and inverse filtering can be used for enhancing images from different types of degradation was proposed. In the color images denoising process, it was very significant for improving the edge and texture information. Eliminating noise can be enhanced by the image quality. In this article, at first, The color images were taken. Then, random noise and blur were added to the images. Then, the noisy blurred image passed to the guided filtering to get on denoised image. Finally, an inverse filter applied to the blurred image by convolution an image with a mask and getting on the enhanced image. The results of this research illustrated good outcomes compared with other methods for removing noise and blur based on PSNR measure. Also, it enhanced the image and retained the edge details in the denoising process. PSNR and SSIM measures were more sensitive to Gaussian noise than blur.

2 citations

Journal ArticleDOI
01 Jul 2021
TL;DR: In this article, a real-time traffic light management scheme using contrast improvement and fluctuating logic systems for morphological operators is proposed, which provides an upgrade over traditional systems in terms of time of response, automation, stability and overall performance.
Abstract: There is a need for development of advanced smart traffic controlling schemes due to the enhancement in urban traffic congestion. Currently, there are some traffic controlling methods based on timers or controlled by human. However, due to these systems there must be wastage of power in the night times and early morning hours. In order to address this issue, this paper introduces a novel and Traffic light management scheme in real-time with digital image recognition using contrast improvement and fluctuating logic systems for morphological operators. The proposed traffic control system provides an upgrade over traditional systems in terms of time of response, automation, stability and overall performance.

2 citations