QR code-based non-linear image encryption using Shearlet transform and spiral phase transform

In this paper, we propose a new quick response (QR) code-based non-linear technique for image encryption using Shearlet transform (ST) and spiral phase transform. The input image is first converted into a QR code and then scrambled using the Arnold transform. The scrambled image is then decomposed into five coefficients using the ST and the first Shearlet coefficient, C₁ is interchanged with a security key before performing the inverse ST. The output after inverse ST is then modulated with a random phase mask and further spiral phase transformed to get the final encrypted image. The first coefficient, C₁ is used as a private key for decryption. The sensitivity of the security keys is analysed in terms of correlation coefficient and peak signal-to noise ratio. The robustness of the scheme is also checked against various attacks such as noise, occlusion and special attacks. Numerical simulation results are shown in support of the proposed technique and an optoelectronic set-up for encryption is also proposed.     

Optical colour image encryption using spiral phase transform and chaotic pixel scrambling

In this study, we propose a new optical colour image encryption technique using spiral phase transform and chaotic pixel scrambling. For encryption, three channels of the colour image i.e. red, green and blue are first separated and modulated with three different structured phase masks. Spiral phase transform (SPT) with a particular order of modified spiral phase function (MSPF) is utilized for further processing. Random modulus decomposition is applied to the complex output after SPT to generate the private key for decryption. The pixels of the image are scrambled by using the chaotic Tinkerbell map for enhanced security. The order of MSPF, three structured phase masks, parameters of Tinkerbell mapping, and the private key generated during the encryption process serve as the security keys. The robustness of the proposed method is checked against various potential attacks. A series of numerical simulation results are presented to validate the proposed colour image encryption method.     

Deep underwater image enhancement through colour cast removal and optimization algorithm

ABSTRACT

Blue–green colour cast effect and low contrast are common problems suffered by deep underwater images. This paper introduces a new method which consists of two major steps: red channel correction based on green and blue channels (RCCGB), and simultaneous contrast stretching and mean pixel enhancement (SCSMPE). The RCCGB is designed to minimize the effect of blue–green illumination. This step considers the differences between the red channel and other channels in terms of total pixel values. The second major step, SCSMPE is specifically designed to perform contrast stretching and improve the mean pixel value simultaneously through particle swarm optimization (PSO). Based on the visual observation, the proposed method significantly reduces the effect of the blue–green colour cast and improves the image contrast. Furthermore, the average quantitative values for 300 underwater images also demonstrate the superiority of the proposed method.     

Three fringe photoelasticity - use of colour image processing hardware to automate ordering of isochromatics

The early application of digital image processing (DIP) technique to automate photoelastic analysis is based on black and white (B & W) image processing systems. The various methodologies reported can be traced to rely upon one or several features of a B &W DIP system. With the advancements in computer technology, nowadays colour image processing systems are available at affordable prices. A systematic study has been carried out on how to use the red green blue (RGB) value recorded using a colour image processing system for determining fringe orders up to 3 in a fringe field. The study has revealed that instead of directly using RGB values corresponding to a dark field image, if the difference of RGB values between bright anddarkfield images is used, the number of noise points is less. Experiments have been carried out for several arbitrary lines in a circular disk under diametral compression and the results are found to be good.     

Underexposed and overexposed colour image enhancement using information set theory

This paper presents a new approach for improving the contrast of digital colour images in extreme lighting environment. The concept of information sets that are derived from fuzzy sets is applied for this purpose. The S and V components of the HSV (hue, saturation, value) colour model are fuzzified using Gaussian and triangular membership functions but H is preserved. Objective measures like contrast information, quality and visual factor (V_f) are defined to represent the image characteristics. A new entropy function and V_f are used to form the objective function which is optimised using particle swarm optimisation to learn the image-specific parameters required for the enhancement. These new measures defined here serve to evaluate the performance of the proposed algorithm. In addition to preserving the colour and specific image features, the subjective and objective evaluations clearly depict the improvement in the quality of both the underexposed and overexposed images. On comparison, it is found that the proposed technique outperforms the existing techniques in terms of entropy, information measure and mean opinion score.     

Automatic defect inspection system of colour filters using Taguchi-based neural network

A coloured filter is a critical part of an LCD panel, especially to present a high quality colour display. At present, the defect detection of colour filters is conducted by manual inspection in the final product stage. However, poor detection efficiency and subjective judgment of manual inspection undermine accuracy. Therefore, this study applied image processing technology and the neural network to detect surface defects of colour filters in order to prevent losses arising from incorrect detection, lower production costs, and effectively improve yield. A back-propagation neural network (BPNN) classifier was selected to train the features. The results showed that the proposed method can be successfully applied in defect detection of colour filters to reduce artificial detection errors. In addition, the Taguchi method was used with BPNN to save time searching optimal learning parameters by the trial and error method, which achieves faster convergence, smaller convergent errors and better recognition rate. The results proved that the root-mean-square error (RMSE) of the Taguchi-based BPNN at final convergence is 0.000254, and recognition rate reaches 94%. Therefore, the proposed method has good effects in detecting the micro defects of a colour filter panel.     

Introducing a simple method to determine aggregate gradation of hot mix asphalt using image processing

The aggregate gradation is one of the most important parameters in the mechanical properties of hot mix asphalt (HMA). Thus, determining the aggregate gradation is a very significant subject in civil engineering. Usually, to estimate the aggregate gradation, it is required to separate the aggregate from the bitumen, and this operation can be time-consuming and even dangerous related to chemical solvents. Moreover, several computer-based methods have been established to model the internal structure of HMA in two- and three-dimensional methods and can be applied to determine the aggregate gradation, but these methods need special and expensive equipment. Therefore, in this study, a simple approach is introduced to quickly and easily determine the aggregate gradation of HMA from the prepared cross-section images of cylindrical samples using numerical and image-processing techniques such as fitting equation and colour space system. The obtained results indicate that the introduced method can detect the aggregate gradation with high accuracy and can be used as a satisfactory alternative to other expensive methods.