Shilong Liu

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.

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.

Multi-focal image fusion using degree of focus and fuzzy logic

Abstract The continuous advancement in the field of imaging sensor necessitates the development of an efficient image fusion technique. The multi-focal image fusion extracts the in-focus information from the source images to construct a single composite image with increased depth-of-field. Traditionally, the information in multi-focal images is divided into two categories: in-focus and out-of-focus data. Instead of using a binary focus map, in this work we calculate the degree of focus for each source image using fuzzy logic. The fused image is then generated based on the weighted sum of this information. An initial focus tri-state map is built for each input image by using spatial frequency and a proposed focus measure named as alternate sum modified Laplacian. For the cases where these measures indicate different source images to contain focused pixel or have equal strength, another focus measure based on sum of gradient is employed to calculate the degree of focus in a fuzzy inference system. Finally, the fused image is computed from the weights determined by the degree of focus map of each image. The proposed algorithm is designed to fuse two source images, whereas fusion of multiple input images can be performed by fusing a source image with the fusion output of the previous input group. The comparison of the proposed method with several transform and pixel domain based techniques are conducted in terms of both subjective visual assessment and objective quantitative evaluation. Experimental results demonstrate that our method can be competitive with or even outperforms the methods in comparison.

Image de-hazing from the perspective of noise filtering

Abstract Digital images captured in outdoor environment are easily polluted by haze, which will degrade the conveyed information. To overcome this problem, a large number of researches have been conducted for image haze removal, among which the approach based on the dark channel prior assumption is in recent years considered as the state-of-the-art. This method is primarily dependent on consolidation of observations. However, in the proposed method, a theoretical perspective is adopted for image, which considers the degraded image as a product contaminated by noise. Two maps are constructed to label the noise severity and atmospheric light. The parameters involved are optimized via Particle Swarm Optimization with a penalty function in terms of hue change. Experimental results were compared with seven available approaches, along with an analysis on algorithm complexity. These analyses verify the effectiveness, efficiency, wide adaptability and theoretical soundness of the proposed approach.

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.

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.

Logarithmic profile mapping and Retinex edge preserving for restoration of low illumination images

Contents carried in an image are valuable information sources for many scientific and engineering applications. However, if the image is captured under low illumination conditions a large portion of the image appears dark and this heavily degrades the image quality. In order to solve this problem, a restoration algorithm is developed here that transforms the low input brightness to a higher value using a logarithmic mapping function. The mapping is further refined by a linear weighting with the input to reduce the un-necessary amplification at regions with high brightness. Moreover, fine details in the image are preserved by applying the Retinex principle to extract and then re-insert object edges. Results from experiments using low and normal illumination images have shown satisfactory performances with regard to the improvement in information contents and the mitigation of viewing artifacts.

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.

Three dimensional shape measurement of wear particle by iterative volume intersection

The morphology of wear particle is a fundamental indicator where wear oriented machine health can be assessed. Previous research proved that thorough measurement of the particle shape allows more reliable explanation of the occurred wear mechanism. However, most of current particle measurement techniques are focused on extraction of the two-dimensional (2-D) morphology, while other critical particle features including volume and thickness are not available. As a result, a three-dimensional (3-D) shape measurement method is developed to enable a more comprehensive particle feature description. The developed method is implemented in three steps: (1) particle profiles in multiple views are captured via a camera mounted above a micro fluid channel; (2) a preliminary reconstruction is accomplished by the shape-from-silhouette approach with the collected particle contours; (3) an iterative re-projection process follows to obtain the final 3-D measurement by minimizing the difference between the original and the re-projected contours. Results from real data are presented, demonstrating the feasibility of the proposed method.