This paper uses a two-step method for edge detection using a polynomial differentiation threshold on contrast-enhanced images. In the first step, to enhance the image contrast, the mean absolute deviation and harmonic mean brightness values of the images are calculated. Mean absolute deviation is used to perform the histogram clipping to restrict over-enhancement. First, the clipped histogram is divided in half, and then two sub-images are created and equalized, and combined into a final image that keeps image quality. The second phase involves edge detection using a polynomial differentiation-based threshold on contrast-improved visuals. The polynomial differentiation curve-fitting method was used to smooth the histogram data. The nearest index value to zero is utilized to calculate the threshold value to detect the edges. The significance of the proposed work is to contrast enhancement of low-light images to extract the edge lines. Its value or merit is to achieve improved edge results in terms of various image quality metrics. The findings of the proposed research work are to detect the edges of low-contrast images. Image quality metrics are computed and it is observed that the suggested algorithm surpasses former methods in respect of Edge-based contrast measure (EBCM), Performance Ratio, F-Measure, and Edge-strength similarity-based image quality metric (ESSIM).

The visual appearance of a digital image can be improved through image enhancement algorithm by reducing the noise in an image, improving the color, brightness and contrast of an image for more analysis. This paper introduces an image enhancement algorithm. The image histogram is processed through multinomial curvature fitting function to reduces the number of pixels for each intensity value through minimizing the sum of squared residuals. Then resampling is done to smooth out the computed data. After then histogram clipping threshold is computed by central moment processed on the resampled data value to restrict the over enhancement rate. Histogram is equally divided into two sub histograms. The sub histograms are equalized by transfer function to merged the sub images into one output image. The output image is further improved by reducing the environmental haze effect by applying Matlab imreducehaze method, which gives the final output image. Matlab simulation results demonstrate that the proposed method outperforms than other compared methods in terms of both quantitative and qualitative performance evaluation applied on colorfulness based PCQI (C-PCQI), and blind image quality measure of enhanced images (BIQME) image quality metrics.

Digital image enhancement is a technique to process a digital image to improve the overall visual quality of image. In this paper, Variance concept based clipping threshold value is computed from input image pixel intensity to limit the rate of over enhancement. The histogram of the original image is sub-divided into five adjacent sections and the boundary values between adjacent sections are put from the penile value of intensity range. Besides, over enhancement of the image is avoided by clipping certain number of pixels having more intensity than the clipping limit and these pixels are rearranged below the clipping limit. The present method offers two advantages viz., clipping of the certain pixels based on the property of the data set itself. The another one is to histogram processing by parts and this has given better visual quality, low computation time with improved metrics related to image enhancement. Histogram of each specific sub-image is equalized independently and then combined to produce the final contrast enhanced image. The final output image is further processed through imreducehaze filter for more improve result. Quantitative evaluation of proposed algorithm is performed by CPCQI and QILV image quality metrics and the simulation results have shown that the proposed variance based histogram equalization algorithm produces better quality of image in terms of contrasts, brightness and color in comparison to the other existing histogram equalization algorithms.

In this paper, skewness and mode-based histogram equalization algorithm have been proposed for contrast enhancement of digital images. The present method gives a novel idea for histogram clipping and histogram bifurcation. The prior is done with the skewness value and the latter is done with help of mode values of the intensity level random data set. The pixel intensity levels are random and thus a stochastic approach has been used and found to yield improved figure of merits. The image histogram has been clipped with the help of a pre-assigned threshold value computed from skewness value to restrict the rate of over enhancement. The clipped histogram is subdivided into two parts, using the histogram subdivision limit which is calculated on the basis of the mode value of the image. Histogram of individual sub-image is equalized independently and then integrated to form the final enhanced image. The simulation results have shown that the proposed skewness and mode based bi-histogram equalization algorithm enhances the contrast of the image in a better manner compared with the other histogram equalization methods in terms of FSIM, PSIM, SFF, VSI, HaarPSI, and GMSD.

The article presents a concept of computer-aided design and three-dimensional (3D) computer animation of a newly proposed magnetically levitation based shoes where the users can move in the air. The aerial movement would be in the direction of a magnetic track which is laid down below the trajectory and the users have to wear the maglev shoes. They can move from ground floor to upper floors as in the case of an elevator. The users have been provided adequate control over the speed of movement and they can stop and run the system by themselves at any instant of time by self-controlling of the motion generated by them. The maglev shoes are proposed to be built with superconducting materials to levitate above the magnetic tracks. Computer vision features are efficiently utilized to detect various features of the animated image frame of maglev shoes and the motion study of the proposed system. Statistical methods with existing functions are used to analyze various features like speed, angles and finding the optical flow in both horizontal and vertical direction of maglev shoe wearers.

This In the present study, a new type of ship is designed based on the theory of magnetic levitation (Maglev). The structural layout of the proposed ship is modified to adopt the maglev technology. The objective of designing this ship is to assist merchandise and travelers over a long distance in a short period of time.  The design and size of this ship is made in such a manner that it has got airplanes landing and take-off facility. According to the theory of magnetic levitation, it travels with negligible friction (ideally frictionless) on a magnetic guideway with high speed and expected safety. An electro-dynamic suspension (EDS) system for magnetic levitation has been used and it would reduce friction on a large scale and generate strong force for both lifting and propulsion purposes and it would also allow the very high speed of travel. High-temperature superconductor (HTS) wires with cryogenic structure are utilized as a part of the configuration of a couple of segments of the maglev ship. The attraction and repulsion force between superconductor magnets and permanent electromagnets are the key factors behind such operation. Computer vision study helps to track the moving object to find whether the object is moving or not. The proposed method has been found to give the acceptable result to identify the moving object.