Measure of image enhancement by parameter controlled histogram distribution using color image

Abstract: Histogram Equalization (HE) technique is simple and effectively used for contrast enhancement. But HE is not suitable for consumer electronic products because it may produces washed out appearance image. The new quantitative measures of image enhancement and frequency domain based methods used for object detection and visualization and enhancement technique driven by both global and local process on luminance and chrominance components of the image. This measure of image enhancement is related with the concepts of Weber's law of the human visual systems. It helps to choose the best parameters and transform. This approach based on parameter controlled histogram distribution method can enhance simultaneously overall contrast and sharpness of an image. This approach also increases the visibility of specified portions or better maintaining image color. This analysis provides better performance for contrast enhancement Index terms: Histogram equalization, Image enhancement, Contrast enhancement, I.INTRODUCTION A digital image is converted into analogy signal which is scanned into a display. Before the processing image is converted into digital format, Digitization includes sampling of image and quantization of sampled values. After converting the image into bit, information is processed. In image processing, images are available in digitized form that is arrays of finite length binary format. For digitization, the given images are sampled on a discrete grid and each sample or pixel is quantized using a finite number of bits. The digitized image is used in computer. Image enhancement which transforms digital images to enhance the visual information. To enhance image contrast is the intensity mapping that reassigns the intensity of pixels through a monotonically increasing function. Primary operation for all vision and image processing tasks in several areas. In forensic video/image analysis tasks surveillance videos have quite different qualities compared with other videos such as the videos for high quality entertainment or TV broadcasting. Enhancement transformation to modify the contrast of an image within a display's dynamic range is therefore required in order to show full information content in the videos. Contrast enhancement is an important function in image processing applications. The objective of this method is to make an image clearly recognized for a specific application. Point operation based enhancement techniques are contrast stretching, non-linear point transformation, histogram modeling. The non-linearity is introduced by many imaging lighting device which can be described with a point operation. Gamma correction is using the power law's light intensity operation [1] which is adjusting the lightness/darkness level of their prints. X=(x) (1/gamma value) Where x is the original pixel value. Depending on the gamma value, image can be lightened or darkened. so, it will improve visual contrast and also decreases the visual contrast too. Histogram Equalization (HE) is most popular technique for contrast enhancement. HE makes uniform distribution of the gray level for an image. But, consumer electronics such as Flat panel display (FPD), HE is rarely applied in directly because the significant changes in brightness [2]. The HE effectiveness is depends on the contrast of the original image. In general, HE will flatten out the probability distribution of an image and increase its dynamic range and also will make the average brightness towards the middle gray level of an image regardless of the input image, and produce the objectionable artifacts and unnatural contrast effect. This makes the visual quality of processed image is unsatisfactory. Surveillance videos have quietly different qualities compared with other videos such as videos for high quality entertainment or T

Optimization of video capturing and tone mapping in video camera systems

Image enhancement techniques are widely employed in many areas of professional and consumer imaging, machine vision and computational imaging. Image enhancement techniques used in surveillance video cameras are complex systems involving controllable lenses, sensors and advanced signal processing. In surveillance, a high output image quality with very robust and stable operation under difficult imaging conditions are essential, combined with automatic, intelligent camera behavior without user intervention. The key problem discussed in this thesis is to ensure this high quality under all conditions, which specifically addresses the discrepancy of the dynamic range of input scenes and displays. For example, typical challenges are High Dynamic Range (HDR) and low-dynamic range scenes with strong light-dark differences and overall poor visibility of details, respectively. The detailed problem statement is as follows: (1) performing correct and stable image acquisition for video cameras in variable dynamic range environments, and (2) finding the best image processing algorithms to maximize the visualization of all image details without introducing image distortions. Additionally, the solutions should satisfy complexity and cost requirements of typical video surveillance cameras. For image acquisition, we develop optimal image exposure algorithms that use a controlled lens, sensor integration time and camera gain, to maximize SNR. For faster and more stable control of the camera exposure system, we remove nonlinear tone-mapping steps from the level control loop and we derive a parallel control strategy that prevents control delays and compensates for the non-linearity and unknown transfer characteristics of the used lenses. For HDR imaging we adopt exposure bracketing that merges short and long exposed images. To solve the involved non-linear sensor distortions, we apply a non-linear correction function to the distorted sensor signal, implementing a second-order polynomial with coefficients adaptively estimated from the signal itself. The result is a good, dynamically controlled match between the long- and short-exposed image. The robustness of this technique is improved for fluorescent light conditions, preventing serious distortions by luminance flickering and color errors. To prevent image degradation we propose both fluorescent light detection and fluorescence locking, based on measurements of the sensor signal intensity and color errors in the short-exposed image. The use of various filtering steps increases the detector robustness and reliability for scenes with motion and the appearance of other light sources. In the alternative algorithm principle of fluorescence locking, we ensure that light integrated during the short exposure time has a correct intensity and color by synchronizing the exposure measurement to the mains frequency. The second area of research is to maximize visualization of all image details. This is achieved by both global and local tone mapping functions. The largest problem of Global Tone Mapping Functions (GTMF) is that they often significantly deteriorate the image contrast. We have developed a new GTMF and illustrate, both analytically and perceptually, that it exhibits only a limited amount of compression, compared to conventional solutions. Our algorithm splits GTMF into two tasks: (1) compressing HDR images (DRC transfer function) and (2) enhancing the (global) image contrast (CHRE transfer function). The DRC subsystem adapts the HDR video signal to the remainder of the system, which can handle only a fraction of the original dynamic range. Our main contribution is a novel DRC function shape which is adaptive to the image, so that details in the dark image parts are enhanced simultaneously while only moderately compressing details in the bright areas. Also, the DRC function shape is well matched with the sensor noise characteristics in order to limit the noise amplification. Furthermore, we show that the image quality can be significantly improved in DRC compression if a local contrast preservation step is included. The second part of GTMF is a CHRE subsystem that fine-tunes and redistributes the luminance (and color) signal in the image, to optimize global contrast of the scene. The contribution of the proposed CHRE processing is that unlike standard histogram equalization, it can preserve details in statistically unpopulated but visually relevant luminance regions. One of the important cornerstones of the GTMF is that both DRC and CHRE algorithms are performed in the perceptually uniform space and optimized for the salient regions obtained by the improved salient-region detector, to maximize the relevant information transfer to the HVS. The proposed GTMF solution offers a good processing quality, but cannot sufficiently preserve local contrast for extreme HDR signals and it gives limited improvement low-contrast scenes. The local contrast improvement is based on the Locally Adaptive Contrast Enhancement (LACE) algorithm. We contribute by using multi-band frequency decomposition, to set up the complete enhancement system. Four key problems occur with real-time LACE processing: (1) "halo" artifacts, (2) clipping of the enhancement signal, (3) noise degradation and (4) the overall system complexity. "Halo" artifacts are eliminated by a new contrast gain specification using local energy and contrast measurements. This solution has a low complexity and offers excellent performance in terms of higher contrast and visually appealing performance. Algorithms preventing clipping of the output signal and reducing noise amplification give a further enhancement. We have added a supplementary discussion on executing LACE in the logarithmic domain, where we have derived a new contrast gain function solving LACE problems efficiently. For the best results, we have found that LACE processing should be performed in the logarithmic domain for standard and HDR images, and in the linear domain for low-contrast images. Finally, the complexity of the contrast gain calculation is reduced by a new local energy metric, which can be calculated efficiently in a 2D-separable fashion. Besides the complexity benefit, the proposed energy metric gives better performance compared to the conventional metrics. The conclusions of our work are summarized as follows. For acquisition, we need to combine an optimal exposure algorithm, giving both improved dynamic performance and maximum image contrast/SNR, with robust exposure bracketing that can handle difficult conditions such as fluorescent lighting. For optimizing visibility of details in the scene, we have split the GTMF in two parts, DRC and CHRE, so that a controlled optimization can be performed offering less contrast compression and detail loss than in the conventional case. Local contrast is enhanced with the known LACE algorithm, but the performance is significantly improved by individually addressing "halo" artifacts, signal clipping and noise degradation. We provide artifact reduction by new contrast gain function based on local energy, contrast measurements and noise estimation. Besides the above arguments, we have contributed feasible performance metrics and listed ample practical evidence of the real-time implementation of our algorithms in FPGAs and ASICs, used in commercially available surveillance cameras, which obtained awards for their image quality

Enhancement of Over-Exposed and Under-Exposed Images Using Hybrid Gamma Error Correction Sigmoid Function

The demands to improve the visibility quality of the captured images in extremes lighting conditions have emerged increasingly important in digital image processing. The extremes conditions are when there is lack of reasonable lightnings termed as underexposed and too much of light termed as overexposed. The popular enhancement technique currently used is the contrast enhancement through contrast stretching, histogram equalization, homomorphic filtering and contrast adjustment. The adjustments are to transform the less useful images to more meaningful images when the post image processing operations are carried out. This thesis is motivated to deal with the problems concerning image capturing in these two extremes conditions. The sigmoid function is used to adjust the contrast with two controlling parameters. The parameters adjust the contrast locally and globally. The gamma function is commonly used to correct the non-linear error in the images due to the camera lenses. This thesis combines the functions' properties and developed a hybrid algorithm to improve the quality of the poorly captured images by adjusting the contrast and compensating the gamma error. The sigmoid and gamma function are coded in MATLAB 6.0 in which testes are made over the selected images. The sample images are taken using different type of cameras transformed to grayscaled input images. The luminosities of the surroundings are also measured using a light meter. The derivations of the parameters' ranges are done by calculating the root mean square error or the standard deviation. The suggested ranges are used in the hybrid system which has two variants, Variant I and Variant 11. The first variant, combines the sigmoid function inside the gamma compensation function while the second variant combines the gamma compensation function inside the sigmoid function. Based on the test results, the proposed algorithm significantly improves the contrast of the images. For the underexposed image samples, the percentages of the intensity lesser than 0.1 decreases as more of the intensities reside at higher values. For the overexposed image samples, the percentages of intensity greater than 0.9 decreases as more of the intensities reside at lower values. With the suggested range deduced, the images are contrast enhanced with the reduction of percentage of pixels residing he intensity less than 0.1 and greater than 0.9. The comparative analyses are made by comparing the suggested hybrid system with the existing adaptive homomorphic filtering, adaptive histogram equalization and adaptive contrast enhancement

Color Image Enhancement Techniques for Endoscopic images

Modern endoscopes play an important role in diagnosing various gastrointestinal (GI) tract related diseases. Although clinical findings of modern endoscopic imaging techniques are encouraging, there still remains much room for improvement of image quality. Of greatest concern, endoscopic images suffer from various degradations, such as specular highlights, non-uniform brightness and poor contrast. As a result, gastroenterologists often face difficulty in successfully identifying the subtle features, such as mucosal surface and structures, pit patterns, size and pattern of micro-vessels, tissue and vascular characteristics, superficial layer of mucosal and abnormal growths in endoscopic images. The improved visual quality of images can provide better diagnosis. This paper presents two proposed post-processing techniques for enhancing the subtle features of endoscopic images. The first proposed technique is named as endoscopic image enhancement based on adaptive sigmoid function and space-variant color reproduction (ASSVCR). It is achieved in two stages: image enhancement at gray level followed by color reproduction with the help of space variant chrominance mapping. Image enhancement is achieved by performing adaptive sigmoid function and uniform distribution of sigmoid pixels. Then color reproduction is used to generate new chrominance components. The second proposed technique is named as tri-scan. It is achieved in three stages: (1) Tissue and surface enhancement: a modified linear unsharp masking is used to sharpen the surface and edges of tissue and vascular characteristics, (2) Mucosa layer enhancement: an adaptive sigmoid function similar to the ASSVCR technique is employed on the R plane of the image to highlight the superficial layers of mucosa, (3) Color tone enhancement: the pixels are uniformly distributed to create a different color effect to highlight mucosa structures, superficial layers of mucosa and tissue characteristics. Both techniques are compared with other related works. Several performance metrics like focus value, statistic of visual representation, measurement of uniform distribution, color similarity test, color enhancement factor (CEF) and time complexity are used to assess the performance. The results showed improved performance compared to similar existing methods. In the post-processed images, we have observed that the ASSVCR can enhance and highlight pit patterns, tissue and vascular characteristics, mucosa structures and abnormal growths. It cannot highlight size and pattern of micro-vessels, and superficial layer of mucosa. In contrast, tri-scan can enhance and highlight all above mentioned features of endoscopic images