Low dynamic range (LDR) image may contain low‐light and highlight areas due to the limitations of the dynamic range of conventional image sensors. Low‐light and highlight phenomena limit colour richness and visibility of objects in an image. Therefore, it can cause a reduction in the quality of images and a loss in accuracy in the application of image recognition. To overcome this, high dynamic range (HDR)‐like images have been developed with rich colours such as those seen by the human eye. In this study, the authors propose a method to obtain an HDR‐like image from a single LDR image by removing the specular component from highlight pixels as well as strengthening the actual colour. Next, they select low‐light image enhancement via illumination map estimation as a low‐light enhancement technique by showing the comparison with gamma‐based expansion operator. They evaluate their HDR‐like output images with non‐reference and full‐reference metrics. They show the comparison of their proposed method with six other methods. Besides, visually, their proposed method delivers more pleasing output than the output of other competitive methods.

The color detection from light emitting diode (LED) array by using smartphone camera is very difficult in visual multiple-input multiple-output (visual-MIMO) system. In this paper, we propose a method to detect the LED color through smartphone camera applying regression analysis. We apply multivariate regression model for detecting LED color. By taking picture of LED array, we detect the region of interest (ROI), then detecting LEDs by image processing algorithm, applying k means-clustering algorithm for generating possible number of colors of LED array and finally applying regression model to predict transmitted LEDs color. We get the value of test database R-squared 91.14%, 94.80% and 96.24% for red, green and blue channel, respectively.

Hardware suitability of an algorithm can only be verified when the algorithm is actually implemented in the hardware. By hardware, we indicate system on chip (SoC) where both processor and field-programmable gate array (FPGA) are available. Our goal is to develop a simple algorithm that can be implemented on hardware where high-level synthesis (HLS) will reduce the tiresome work of manual hardware description language (HDL) optimization. We propose an algorithm to achieve high dynamic range (HDR) image from a single low dynamic range (LDR) image. We use highlight removal technique for this purpose. Our target is to develop parameter free simple algorithm that can be easily implemented on hardware. For this purpose, we use statistical information of the image. While software development is verified with state of the art, the HLS approach confirms that the proposed algorithm is implementable to hardware. The performance of the algorithm is measured using four no-reference metrics. According to the measurement of the structural similarity (SSIM) index metric and peak signal-to-noise ratio (PSNR), hardware simulated output is at least 98.87 percent and 39.90 dB similar to the software simulated output. Our approach is novel and effective in the development of hardware implementable HDR algorithm from a single LDR image using the HLS tool.

The paper has discussed the image fusion using add weighted algorithm based on the pixel intensity clustering algorithm (PICA) and radial basis function (RBF) neural network thresholding method. It is a comparative study when we apply add weighted fusion algorithm with and without applying PICA and RBF neural network thresholding algorithm. The PICA and RBF neural network are applied to grayscale IR image to extract the object by the determination of threshold value for the IR image. After applying the PICA and RBF we will have the IR image where object and background are separated. Now we fuse this grayscale IR image where the object is separated from the background with the visible image at different weights and try to find out the proper weight for the fusion. We also fuse the raw IR image with the visible image to compare with our result. The raw IR image means an image where we do not apply any PICA and RBF neural network algorithm. From the comparison, we can have the object with better resolution background when we fuse the IR image after applying PICA and RBF.

This paper is about determining the threshold for a gray scale IR image. At first, Pixel Intensity Clustering Algorithm (PICA) is used to have the center for the Radial Basis Function (RBF) neural network. PICA clusters the image into a defined number. Every group has a centroid which has been used as RBF neural network center. The number of hidden neurons will also be equal to the number of clusters. We give the weight to the output of the hidden neurons on the base of the desired output and the neural network output will be a threshold value for the corresponding gray scale image. The simulation result gives us a clear idea about the desired result.