Guannan Jiang

Image enhancement using the averaging histogram equalization (AVHEQ) approach for contrast improvement and brightness preservation

Display Omitted A histogram equalization method is proposed to preserve original image brightness.A histogram averaging technique is developed to recover image information lost.A histogram remapping technique is used to reduce artifacts introduced to images.An optimization minimizes the brightness change between input and output images.Results show that image brightness is preserved while image contrast is enhanced. Image contrast enhancement and brightness preservation are fundamental requirements for many vision based applications. However, these are two conflicting objectives when the image is processed by histogram equalization approaches. Current available methods may not provide results simultaneously satisfying both requirements. In this work, a pipelined approach including color channel stretching, histogram averaging and re-mapping is developed. By using stretching, color information from a scene is restored. Averaging against a uniform distribution enables the output image to recover the information lost. Furthermore, histogram re-mapping reduces artifacts that often arise from the equalization procedure. The technique also employs a search process to find optimal algorithmic parameters, such that the mean brightness difference between the input and output images is minimized. The effectiveness of the proposed method was tested with a set of images captured in adverse environments and compared against available methods. High performing qualitative and quantitative results were obtained.

Image contrast enhancement with brightness preservation using an optimal gamma correction and weighted sum approach

The enhancement of image contrast and preservation of image brightness are two important but conflicting objectives in image restoration. Previous attempts based on linear histogram equalization had achieved contrast enhancement, but exact preservation of brightness was not accomplished. A new perspective is taken here to provide balanced performance of contrast enhancement and brightness preservation simultaneously by casting the quest of such solution to an optimization problem. Specifically, the non-linear gamma correction method is adopted to enhance the contrast, while a weighted sum approach is employed for brightness preservation. In addition, the efficient golden search algorithm is exploited to determine the required optimal parameters to produce the enhanced images. Experiments are conducted on natural colour images captured under various indoor, outdoor and illumination conditions. Results have shown that the proposed method outperforms currently available methods in contrast to enhancement and brightness preservation.

Histogram equalization and optimal profile compression based approach for colour image enhancement

Display Omitted A pipeline approach to increase contrast and restore vividness to colour images.Pre-processing procedures include colour stretching, and histogram equalization.A magnitude compression and a saturation maximization stage as post processing.Image content based feedback provides optimal compression to reduce artefacts.Comprehensive assessment shows improvements on contrast and colour vividness. Many vision based applications depend on images with sufficiently high contrast and colourfulness so that ample amount of information is available to accurately describe objects captured in an image scene. Poor image capturing conditions are often unavoidable but can be compensated. Approaches based on intensity histogram equalization are popular to increase the information content within an image but over-enhancement often results in the production of unwanted artefacts. Furthermore, when constrained to only an intensity-based enhancement, insufficient enrichment on colourfulness and saturation is often observed. In order to address these limitations concurrently, a pipelined approach that incorporates a colour channel stretching process, a histogram equalization step, a magnitude compression procedure, and a saturation maximization stage is proposed. Quantitative and qualitative results obtained from experiments on a wide variety of natural scene images demonstrate the effectiveness of the proposed approach over other methods at reducing artefact while increasing image contrast and colourfulness.

Image contrast enhancement using histogram equalization with maximum intensity coverage

The histogram equalization process is a simple yet efficient image contrast enhancement technique that generally produces satisfactory results. However, due to its design limitations, output images often experience a loss of fine details or contain unwanted viewing artefacts. One reason for such imperfection is a failure of some techniques to fully utilize the allowable intensity range in conveying the information captured from a scene. The proposed colour image enhancement technique introduced in this work aims at maximizing the information content within an image, whilst minimizing the presence of viewing artefacts and loss of details. This is achieved by weighting the input image and the interim equalized image recursively until the allowed intensity range is maximally covered. The proper weighting factor is optimally determined using the efficient golden section search algorithm. Experiments had been conducted on a large number of images captured under natural indoor and outdoor environment. Results showed that the proposed method is able to recover the largest amount of information as compared to other current approaches. The developed method also provides satisfactory performances in terms of image contrast, and sharpness.

Dark channel prior based image de-hazing: A review

Digital images captured under poor environments are vulnerably degraded in their capacities to convey adequate amount of information to the viewer or computer-based processes. One of the common causes affecting the quality of outdoor images can be traced to that coming from atmospheric condensations such as fog or haze. Image processing algorithms, hence, had been developed to address the de-hazing problem in order to recover the scene information. Approaches based on the dark channel prior, in particular, had initiated a large number of research activities because of its satisfactory performance and possibilities for further improvements and applications. In this paper, a review on methods based on the dark channel prior is presented. The principle of restoration by a ray transmission model applied in image de-hazing is examined together with a classification of the models commonly employed. The difficulties encountered in the implementation of de-hazing algorithms are addressed and discussed. A summary of critical issues and a discussion of future trends are also included in this review.

Multisensor multichannel image fusion based on fuzzy logic

The quality of fused images mostly depends on the number of source images available. On the other hand, the optimal fusion of large numbers of images still remains as a nontrivial process. This paper presents a fuzzy logic based method which is able to mitigate such a difficulty. In our approach, we first aligned the spatial resolutions of inputs to the desired value and satisfied the histogram matching requirements. Then we fused images from multiple sensors as a weighted sum of the inputs where the weights were calculated using an entropy and intensity guided fuzzy inference system. Experimental results showed that the proposed method had enhanced image qualities over the original inputs both from the spatial and spectral perspectives.

Image contrast enhancement based on intensity expansion-compression

Contrast enhancement for digital color image using a new approach.Identified cause of information content loss in conventional histogram equalization.Cause of generation of viewing artefacts identified.Intensity range fully utilized to carry scene information by expansion-compression.Preservation of features in the original image. In most image based applications, input images of high information content are required to ensure that satisfactory performances can be obtained from subsequent processes. Manipulating the intensity distribution is one of the popular methods that have been widely employed. However, this conventional procedure often generates undesirable artifacts and causes reductions in the information content. An approach based on expanding and compressing the intensity dynamic range is here proposed. By expanding the intensity according to the polarity of local edges, an intermediate image of continuous intensity spectrum is obtained. Then, by compressing this image to the allowed intensity dynamic range, an increase in information content is ensured. The combination of edge guided expansion with compression also enables the preservation of fine details contained in the input image. Experimental results show that the proposed method outperforms other approaches, which are based on histogram divisions and clippings, in terms of image contrast enhancement.

Image de-hazing based on optimal compression and histogram specification

Haze in the environment will hinder the accurate recognition of objects captured in an image. To overcome this problem, image de-hazing processes have been an active technique applied in many research work. Among the available approaches, the one based on the assumption of dark channel prior is able to produce promising results and improved processing speed by integrating the guided filter. However, there are still some limitations existing in this method; particularly the over-range problem makes the appearance of recovered image unnatural. Moreover, its incapability in preserving image brightness frequently requires user intervention. In order to alleviate these shortcomings, the approach presented in this paper is realized through an effective magnitude compression operation. Histogram specification is further exercised for image post-processing. Finally, parameters of both steps are optimized with the particle swarm optimization algorithm. Experiments were conducted with one hundred and thirty hazy images captured in different environmental conditions. Results showed that the proposed method performs better or equivalently in image de-hazing comparing with the approach based on dark channel prior.

Basic Shape Classification Using Spatially Normalised Fourier Shape Signature

Fourier Descriptors (FD) generation depends heavily on the input shape signature and is a core component in traditional Content Based Image Retrieval (CBIR) systems. This paper presents a novel basic shape classifier developed using Complex Coordinates (CC) FD. A spatial domain normalisation of the FD is achieved by overlaying a Fourier Synthesised Boundary (FSB) against its original Boundary Points (BP). This process creates Intersection Points (IP). A new shape signature is formed using a ratio representing the number of IP over the number of BP. The shape signature coined as Spatially Normalised Fourier Shape Signature (SNFSS), varies from 0 to 1 with increasing number of FD used, and exhibit key trends for the detection of basic shapes like circle and regular polygons. The trends are proven experimentally to be invariant to scale and rotation, as well as being robust to noise.

Radial fourier analysis (RFA) image descriptor

This article presents a spectrum-based local image descriptor, namely, Radial Fourier Analysis (RFA) image descriptor. The RFA descriptor uses Fourier transform to convert the image gradients in the local region of a keypoint from spatial domain to frequency domain. The transformed gradient frequencies are then analysed to obtain the principle description within the local region. The principle description is represented by low frequency Fourier coefficients and they are directly used in the descriptor to represent the keypoint. Experimental results using a series of performance evaluation procedures showed that RFA descriptor demonstrates higher performances against different image variations comparing to benchmark local image descriptors. The results also indicated that the RFA descriptor is particularly reliable when used on the images, which are degraded by blurring and JPG compression. According to the performance evaluations presented in this paper, spectral analysis shows strong potential for local image description. It paves the way for future research in alternative spectrum-based techniques such as Wavelet transform to precisely analyse local image patches.