Histogram equalization (HE) is one of the common methods used for improving contrast in digital images. However, this technique is not very well suited to be implemented in consumer electronics, such as television, because the method tends to introduce unnecessary visual deterioration such as the saturation effect. One of the solutions to overcome this weakness is by preserving the mean brightness of the input image inside the output image. This paper proposes a new method, known as brightness preserving dynamic histogram equalization (BPDHE), which is an extension to HE that can produce the output image with the mean intensity almost equal to the mean intensity of the input, thus fulfill the requirement of maintaining the mean brightness of the image. First, the method smoothes the input histogram with one dimensional Gaussian filter, and then partitions the smoothed histogram based on its local maximums. Next, each partition will be assigned to a new dynamic range. After that, the histogram equalization process is applied independently to these partitions, based on this new dynamic range. For sure, the changes in dynamic range, and also histogram equalization process will alter the mean brightness of the image. Therefore, the last step in this method is to normalize the output image to the input mean brightness. Our results from 80 test images shows that this method outperforms other present mean brightness preserving histogram equalization methods. In most cases, BPDHE successfully enhance the image without severe side effects, and at the same time, maintain the mean input brightness¹.

Many histogram equalization based methods have been introduced for the use in consumer electronics in recent years. Yet, many of these methods are relatively complicated to be implemented, and mostly require a high computational time. Furthermore, some of the methods require several predefined parameters from the user, which make the optimal results cannot be obtained automatically. Therefore, this paper presents bi-histogram equalization with a plateau level (BHEPL) as one of the options for the system that requires a short processing time image enhancement. First, BHEPL divides the input histogram into two independent sub-histograms. This is done in order to maintain the mean brightness. Then, these sub-histograms are clipped based on the calculated plateau value. By doing this, excessive enhancement can be avoided. Experimental results show that this method only requires 34.20 ms, in average, to process images of size 3648 × 2736 pixels (i.e. 10 Mega pixels images). The proposed method also gives better enhancement results as compared with some multi-sections mean brightness preserving histogram equalization methods.

This paper presents a literature survey on existing disparity map algorithms. It focuses on four main stages of processing as proposed by Scharstein and Szeliski in a taxonomy and evaluation of dense two-frame stereo correspondence algorithms performed in 2002. To assist future researchers in developing their own stereo matching algorithms, a summary of the existing algorithms developed for every stage of processing is also provided. The survey also notes the implementation of previous software-based and hardware-based algorithms. Generally, the main processing module for a software-based implementation uses only a central processing unit. By contrast, a hardware-based implementation requires one or more additional processors for its processing module, such as graphical processing unit or a field programmable gate array. This literature survey also presents a method of qualitative measurement that is widely used by researchers in the area of stereo vision disparity mappings.

This paper presents a simple, yet efficient way to remove impulse noise from digital images. This novel method comprises two stages. The first stage is to detect the impulse noise in the image. In this stage, based on only the intensity values, the pixels are roughly divided into two classes, which are "noise-free pixel" and "noise pixel". Then, the second stage is to eliminate the impulse noise from the image. In this stage, only the "noise-pixels" are processed. The "noise-free pixels " are copied directly to the output image. The method adaptively changes the size of the median filter based on the number of the "noise-free pixels " in the neighborhood. For the filtering, only "noise-free pixels " are considered for the finding of the median value. The results from 100 test images showed that this proposed method surpasses some of the state-ofart methods, and can remove the noise from highly corrupted images, up to noise percentage of 95%. Average processing time needed to completely process images of 1600times1200 pixels with 95% noise percentage is less than 2.7 seconds. Because of its simplicity, this proposed method is suitable to be implemented in consumer electronics products such as digital television, or digital camera.

A multi-camera system combines features from different cameras to exploit a scene of an event to increase the output image quality. The combination of two or more cameras requires prior settings in terms of calibration and architecture. Therefore, this paper surveys the available literature in terms of multi-camera systems’ physical arrangements, calibrations, algorithms, and their advantages and disadvantages. We also survey the recent developments and advancements in four areas of multi-camera system applications, which are surveillance, sports, education, and mobile phones. In the surveillance system, the combination of multiple heterogeneous cameras and the discovery of Pan-Tilt-Zoom (PTZ) and smart cameras have brought tremendous achievements in the area of multi-camera control and coordination. Different approaches have been proposed to facilitate effective collaboration and monitoring among the camera network. Furthermore, the application of multi-cameras in sports has made the games more interesting in the aspect of analyses and transparency. The application of the multi-camera system in education has taken education beyond the four walls of the class. The method of teaching, student attendance enrollment, determination of students’ attention, teacher and student assessment can now be determined with ease, and all forms of proxy and manipulation in education can be reduced by using a multi-camera system. Besides, the number of cameras featuring on smartphones is gaining noticeable recognition. However, most of these cameras serve different purposes, from zooming, telephoto, and wider Field of View (FOV). Therefore, future smartphones should be expecting more cameras or the development would be in a different direction.

This paper presents a new fuzzy switching median (FSM) filter employing fuzzy techniques in image processing. The proposed filter is able to remove salt-and-pepper noise in digital images while preserving image details and textures very well. By incorporating fuzzy reasoning in correcting the detected noisy pixel, the low complexity FSM filter is able to outperform some well known existing salt-and-pepper noise fuzzy and classical filters.