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Light is scattered and absorbed when travelling through atmosphere particles, leading to visibility attenuation for images captured, especially in hazy scenes. In addition, hazy images may suffer from color distortion caused by haze or sandstorm, resulting in a poor visual quality. In order to effectively enhance visibility and correct possible color casts for such images, we propose a new image dehazing algorithm based on an improved haze optical model, which consists of three modules: airlight white correction (AWC), local light filter (LLF), and aerial perspective prior (APP). In the proposed algorithm, the AWC module detects and corrects possible color cast, the LLF module downplays non-hazy bright pixels (e.g., headlight and white objects) for more accurate airlight estimation, and the APP module uses the minimum/maximum channel and their difference for scene transmission estimation. The experimental results demonstrate that the proposed method outperforms other state-of-the-art dehazing methods in three ways: 1) our results have better visual quality; 2) our method performs the best in terms of color restoration; and 3) our method is very efficient at removing haze and color casts.

The histogram in each patch of the input image is a useful feature applied for various development of image processing techniques. However, if the size of the input image is very large, the histogram construction of each patch in the image becomes very time-consuming. For applications involving the processing of several very large images, this paper proposes a superior patchwise histogram construction algorithm based on cloud-computing architecture that is faster than similar state-of-the-art approaches. Through the modern communication network, the computation cost can be easily shared to construct several patchwise histograms at the same time. The proposed algorithm is the fastest solution in the field as well as applicable to various data processing procedures related to probability distribution. Experimental results show that the proposed algorithm has the best performance compared to other related algorithms.

The dark channel prior has been considered to be an efficient dehazing technique in recent years. However, its operation causes annoying halo effects. To solve this problem, many former imaging filters have been proposed and combined with the dark channel prior operation. However, these filters inevitably induce enormous computational burden while the dehazing effect of the dark channel prior still has room for improvement. To cope with this, a high-speed refinement method based on the gain intervention is proposed and combined with the dark channel prior to solve the aforementioned problems. As demonstrated in our experiments, the proposed filter integrated into the dark channel prior yields not only higher processing speeds but also superior recovery effects than can previous state-of-the-art imaging filters. More importantly, the dark channel prior combined with the proposed filter possesses the highest potential for practical application due to its superior dehazing effect and time complexity.

For the modern multimedia devices, display resolution and image quality are actively improved nowadays. Although such improvement can produce the high visual perception for the observer, the power consumption becomes an inevasible problem as it is rising progressively. In order to achieve a good balance between visual perception and power consumption, we propose a histogram-based power saving algorithm to improve the image contrast for OLED display panels. The proposed algorithm modifies the empty bins of the image histogram as a pre-process of power reduction. Furthermore, the visual effect is compensated using the power saving histogram equalization algorithm. Experimental results show that the proposed algorithm not only decreases the display power to be lower than that of compared algorithms, but also generates the highly perceptual contrast of the images.

Contrast enhancement is usually applied to those images captured in poor lighting conditions for improving the visual quality. Using interpixel contextual information, a 2-D histogram based contrast enhancement (CE) was proposed to improve image contrast and preserve more details as well. In order to maintain the balance between contrast enhancement and detail preservation, the window size of a 2-D histogram-based contrast enhancement should be adjustable based on the original image contrast and details. In addition, the computation intensive 2-D histogram based CE should be accelerated for real-time applications. Thus, we propose an efficient dynamic window size 2-D histogram construction algorithm in this paper. The proposed algorithm divides the input image into sub-blocks and assigns them appropriate window sizes, which depend upon the standard deviation and the number of distinct intensity values of each individual sub-block. Furthermore, the integral histogram is employed to be able to compute the dynamic range 2-D histogram in constant time while fluctuant window size is adopted dynamically. Experimental results demonstrate the efficacy and efficiency of the proposed algorithm.

Sudden changes in illumination often occur in real world scenarios and may cause considerable difficulties in modeling backgrounds for the state-of-the-art background subtraction methods. In this paper, we propose a simple and effective background re-initialization method that detects sudden luminance change effectively. The purpose of the proposed method is not on the presentation of a specific solution for object detection, but is instead the improvement of the background subtraction approach so that it is capable of sudden luminance change adaptation. Two embodiments related to background subtraction, and which are based on the proposed method, are also presented. These embodiments can detect the moving objects accurately as the luminance of the background model is adjusted quickly after the proposed method is employed for generating the background model. Experimental results demonstrate that the proposed method effectively improves the background subtraction methods as measured by qualitative as well as quantitative assessments.

This paper proposes a new median filter using prior information to capture natural pixels for restoration. In addition to being very efficient in logic execution, the proposed filter restores corrupted images with 1-99% levels of salt-and-pepper impulse noise to satisfactory ones. Without any iteration for noise detection, it intuitively and simply recognizes impulse noises, while keeping the others intact as nonnoises. Depending on different noise ratios at an image, two different sets of masked pixels are employed separately for the adoption of candidates for median finding. Furthermore, no limit to the size of mask windows assures that a proper median can be found. The simple logic of the proposed algorithm achieves significant milestones on the fidelity of a restored image. Moreover, the very fast execution speed of the proposed filter is very suitable for being applied to real-time processing. Relevant experimental results on subjective visualization and objective digital measure are reported to validate the robustness of the proposed filter.

Contrast enhancement involves transforming the intensity of pixels from the original state to feature significant impaction on many display devices, including laptops, PDAs, monitors, mobile camera phones, and so on. This paper proposes a new method to enhance the contrast of the input image and video based on Bezier curve. In order to enhance the quality and reduce the processing time, control points of the mapping curve are automatically calculated by Bezier curve which performs in dark and bright regions separately. Using the fast and accurate histogram modification allows the proposed method to transform the intensity well for both image and video. Experimental results demonstrate the effectiveness of the proposed method in providing a promising enhancement outcome with low computational cost.

This paper proposes a pyramidal background matching structure for motion detection. The proposed method utilizes spectral, spatial, and temporal features to generate a pyramidal structure of the background model. After performing the background subtraction based on the proposed background model, the moving targets can be accurately detected at each frame of the video sequence. In order to produce high accuracy for the motion detection, the proposed method also further includes a noise filter based on Bezier curve to smooth noise pixels, after which the binary motion mask can be computed by the proposed threshold function. Experimental results demonstrate that the proposed method substantially outperforms existing methods by perceptional evaluation.

In recent decades, image and video compression was widely used on network access. However, there are few researches focused on the behavior between data transmission and multimedia compression. Therefore, this paper considers this problem between the encoding of compression and transmission to develop a low bit rate transmission scheme based on Huffman encoding. The proposed method can balance “0” and “1” bits to save the issue by analyzing the probability of the miss match in the typical Huffman tree. Moreover, the proposed method also can modify the transitional tree under the same compression ratio. Experimental results show that the proposed method can reduce the stuffing bits to 51.13% of standard JPEG compression. Besides, the file size after the proposed encoding is the same with the original one. It is observed that the proposed method provides a way to reduce the transmitted bits under the same compression ratio.

Automatic video surveillance is of critical importance to security in commercial, law enforcement, military, and many other environments due to terrorist activity and other social problems. Generally, motion detection plays an important role as the threshold function of background and moving objects in video surveillance systems. This paper proposes a novel motion detection method with a background model module and an object mask generation module. We propose a self-adaptive background matching method to select the background pixel at each frame with regard to background model generation. After generating the adaptive background model, the binary motion mask can be computed by the proposed object mask generation module that consists of the absolute difference estimation and the Cauchy distribution model. We analyze the detection quality of the proposed method based on qualitative visual inspection. On the other hand, quantitative accuracy measurement is also obtained by using four accuracy metrics, namely, Recall, Precision, Similarity, and F1 . Experimental results demonstrate the effectiveness of the proposed method in providing a promising detection outcome and a low computational cost.

Background subtraction involves generating the background model from the video sequence to detect the foreground and object for many computer vision applications, including traffic security, human-machine interaction, object recognition, and so on. In general, many background subtraction approaches cannot update the current status of the background image in scenes with sudden illumination change. This is especially true in regard to motion detection when light is suddenly switched on or off. This paper proposes an illumination-sensitive background modeling approach to analyze the illumination change and detect moving objects. For the sudden illumination change, an illumination evaluation is used to determine two background candidates, including a light background image and a dark background image. Based on the background model and illumination evaluation, the binary mask of moving objects can be generated by the proposed thresholding function. Experimental results demonstrate the effectiveness of the proposed approach in providing a promising detection outcome and low computational cost.

In this paper, we propose a novel background subtraction approach in order to accurately detect moving objects. Our method involves three important proposed modules: a block alarm module, a background modeling module, and an object extraction module. The block alarm module efficiently checks each block for the presence of either a moving object or background information. This is accomplished by using temporal differencing pixels of the Laplacian distribution model and allows the subsequent background modeling module to process only those blocks that were found to contain background pixels. Next, the background modeling module is employed in order to generate a high-quality adaptive background model using a unique two-stage training procedure and a novel mechanism for recognizing changes in illumination. As the final step of our process, the proposed object extraction module will compute the binary object detection mask through the applied suitable threshold value. This is accomplished by using our proposed threshold training procedure. The performance evaluation of our proposed method was analyzed by quantitative and qualitative evaluation. The overall results show that our proposed method attains a substantially higher degree of efficacy, outperforming other state-of-the-art methods by Similarity and F1 accuracy rates of up to 35.50% and 26.09%, respectively.

Gray-level clustering is an important procedure in image processing, which reduces the gray-level intensity of an image. In order to display a high gray-level image on low gray-level device screen, a good gray-level clustering reduction algorithm is necessary to complete this task. Based on the mean values and standard deviations of image histogram within different sub-intervals, a recursive algorithm for the gray-level reduction is proposed in this paper. It divides the image histogram into different sub-intervals recursively until the difference between original image and clustered image within given thresholds are reached. We experimented our proposed algorithm in comparison with other state-of-the-art algorithms on different high gray-level images. Our experimental results show our proposed algorithm outperformed others' in terms of high visual quality of clustered images and computational inexpensiveness.

The use of image contrast enhancement has become increasingly essential due to the need to better show the visual information contained within the image for all vision-based systems. This has lead to motivation for the design of a powerful and accurate automatic contrast enhancement for a digital image. Histogram equalization is the most commonly used contrast enhancement method. However, the conventional histogram equalization methods usually result in excessive contrast enhancement, which causes the unnatural look and visual artifacts of the processed image. In this paper, we propose a novel histogram equalization method using the automatic histogram separation along with the piecewise transformed function. The contrast enhancement results of the proposed method were not only analyzed through qualitative visual inspection and for quantitative accuracy, but are also compared to the results of other state-of-the-art methods.

In this paper, we propose a novel scheme of Local Histogram Equalization (LHE) using guided image filter. The sub-blocks are segmented from the input image, after which we individually compute each local histogram used for multiple transformation functions. To further remove the block artifacts caused by LHE, we employ the input image to guide the texture information, while keeping the enhancement effect of the local contrast. Experimental results show that our proposed scheme produce the high visual effects on the enhanced images being much better than other HE-based methods.

IR LEDs are widely employed in the Internet protocol cameras to increase the imaging effects on the very dark scenes. Hence, the production yield of IR LEDs significantly affects the performance of displaying effects for IP cameras. In this paper, we propose a very efficient way to estimate the IR LED production yield based on image processing technique. Experimental results show that our method effectively improves the working efficiency of the operators on the production line.

The Towers of Hanoi problem is a classical problem in puzzles, games, mathematics, data structures, and algorithms. In this letter, a least memory used algorithm is proposed by combining the source array and target array for comparing the sizes of disk and labeling the disks in the towers of Hanoi problem. As a result, the proposed algorithm reduces the space needed from 2n+2 to n+5, where n represents the disks number.

The local histogram is a key feature that the intensity probability of each pixel. As the neighbours of each pixel must be visited, the construction of local histogram in the big image data is time-consuming. In this paper, we propose a new local histogram construction algorithm based on cloud-computing. Through a real-time communication network, the computation cost can be easily shared to construct several local histograms at the same time. The proposed algorithm is not only the fastest solution in the field, but also applicable to various data processing related to probability distribution. Experimental results show that the proposed algorithm has a best performance compared to other related algorithms.

The non-local means (NLM) algorithm computes the weighted average of the difference between two local patches centred at each neighbour and the centred pixel, which is adapted for the adaptive mean filter of noise suppression. However, computation of the weighted average of the local patch difference is very time consuming. To solve this problem, a fast weighted average computation using an integral image for the NLM, called integral NLM (INLM), is proposed. The performance of the INLM algorithm is estimated and the experimental results show that the outcomes of INLM are totally the same as those by the classical NLM, while the running time is reduced significantly.

In this paper, a power-saving method for emissive display by shrinking histogram is proposed. Based on a modern pixel-level power model of an OLED module, the power consumption factor can be employed in the objective function. Nevertheless, contrast enhancement intrinsically contradicts saving power. In order to solve this problem, we formulate a new objective function which is subject to the constant entropy. By minimizing the distance between two near non-empty bins of image histogram, the power reduction and entropy preservation are simultaneously achieved. To further enhance the perceptional quality, the proposed method is also integrated with other related algorithms. Experimental results show that the proposed method is capable of reducing display power, while the performance of contrast enhancement is also improved.

Objects need to be analyzed in the video surveillance system, while motion detection can be applied to define the analyzable area. This paper proposes a novel motion detection algorithm with background model generation. In order to accurately generate background model, 4-connectivity function is directly used to approximately label the background region. The labeling function makes the background model self-adaptive as object pixels can be roughly ignored for updating. Similarly, this function is also applied to approximately select the foreground region after background subtraction. Finally, objects can be detected by direct threshold function from the labeled foreground region. For measuring the quantitative accuracy, Similarity and F1 are used as two accuracy metrics. As a result, the proposed method produces a substantial degree of efficacy higher than those produced by other state-of-the-art methods.

This paper proposes an efficient method to modify histograms and enhance contrast in digital images. Enhancement plays a significant role in digital image processing, computer vision, and pattern recognition. We present an automatic transformation technique that improves the brightness of dimmed images via the gamma correction and probability distribution of luminance pixels. To enhance video, the proposed image-enhancement method uses temporal information regarding the differences between each frame to reduce computational complexity. Experimental results demonstrate that the proposed method produces enhanced images of comparable or higher quality than those produced using previous state-of-the-art methods.

This paper presents an error-correction scheme to enhance the performance of typical CAN bus. The proposed scheme uses Reed-Solomon (R-S) codec to calculate the parity for the transmission of typical CAN bus. Compared with prior work in terms of Hybrid Automatic Repeat Request (HARQ) scheme for CAN bus transmission, the proposed scheme does not modify the standard CAN protocol but insert an R-S codec unit as an error-correction between Electronic Control Unit (ECU) and CAN bus. In other words, the focus of our proposed scheme is not on modifying the fixed CRC codec of the standard structure, but on increasing the performance based on an additional enhancement module. Experimental results show that the execution time of standard CAN bus can be reduced by the proposed scheme for almost half of transmission time on typical design, while accompanying very minor cost when errors are not correctable or without errors.

In the last decade, improvement of the visual image quality has been actively developed using the contrast enhancement techniques due to the need to better show the visual information contained within the image for all vision-based systems. Histogram equalization is the most commonly used contrast enhancement method. However, the conventional histogram equalization methods usually result in excessive contrast enhancement, which causes the unnatural look and visual artifacts of the processed image. In this paper, we propose a novel histogram equalization method using the precise histogram separation along with the piecewise transformed function. The contrast enhancement results of the proposed method were not only analyzed through qualitative visual inspection and for quantitative accuracy, but are also compared to the results of other state-of-the-art methods.

Median filtering computation for noise removal is often used in impulse noise removal techniques, but the difficulties in removing high-density noise aspect restrict its development. In this paper, we propose a very efficient method to restore image corrupted by high-density impulse noise. First, the proposed method detects both the number and position of the noise-free pixels in the image. Next, the dilatation operation of the noise-free pixels based on morphological image processing is iteratively executed to replace the neighbor noise pixels until convergence. By doing so, the proposed method is capable to remove high-density noise and therefore reconstruct the noise-free image. Experimental results indicate that the proposed method more effectively removes high-density impulse noise in corrupted images in comparison with the other tested state-of-the-art methods. Additionally, the proposed method only requires moderate execution time to achieve optimal impulse noise removal.

In this letter, we propose a novel motion detection method in order to accurately perform the detection of moving objects in the automatic video surveillance system. Based on the proposed Background Generation Mechanism, the presence of either moving object or background information is firstly checked in order to supply the selective updating of the high-quality adaptive background model, which facilitates the further motion detection using the Laplacian distribution model. The overall results of the detection accuracy will be demonstrated that our proposed method attains a substantially higher degree of efficacy, outperforming the state-of-the-art method by average Similarity accuracy rates of up to 56.64%, 27.78%, 50.04%, 43.33%, and 44.09%, respectively.

In this reported work, the lowest level channel prior is proposed for image fog removal. The use of the lowest level channel is simplified from the dark channel prior. It is based on a key observation that fog-free intensity in a colour image is usually the minimum value of trichromatic channels. To estimate the transmission model, the dark channel prior then performs as a min filter for the lowest intensity. However, the min filter results in halo artefacts, specifically for neighbours of edge pixels. Instead of the min filter, this work utilises the exact O (1) bilateral filter to solve this problem. Experimental results show the high performance of the proposed method.