Hybrid model for preserving brightness over the digital image processing

Contrast enhancement and brightness preservation of the image are two important issues of image enhancement in research field now-a-days. The objective is to enhance the image uniformly over different parts of the image. General Histogram Equalization doesn’t control degree of enhancement of the image. To overcome this drawback, another variant of Histogram Equalization method namely Adaptive Bi-histogram Equalization using Threshold (ABHET) is being proposed. The proposed method undergoes three steps, such as: Histogram segmentation using threshold, Clipping of histogram using mean value of occupied intensity and histogram equalization of each sub-images. Finally all the sub-images are combined into one complete image. Simulation results show that ABHET outperforms other existing HE-based methods and different image quality measures such as: Peak signal to noise ratio (PSNR), Absolute Mean Brightness Error (AMBE) and Structural Similarity Index (SSIM) are being used to test the robustness of the proposed method in terms of enhancement of contrast and preservation of brightness.

There are many techniques of image enhancement as well as optimizing the same process in the image processing such as Powell’s method, Fast Ostu’s method, Histogram equalization (HE) method, Particle swarm optimization (PSO) and Honey bee (HB) algorithm. In this paper, a new approach to automatic image enhancement using honey bee mating optimization is implemented by specifying intensity of the edges pixels and also earlier reported PSO results were used. Further results of previous work i-e PSO and HBMO are compared through different parameters. The obtained results indicate that the proposed Honey bee algorithm yields better results in the terms of both the maximization of number of the pixels in the edges and peak signal to noise ratio (PSNR). Computational time is also relatively small in the Honey bee enhanced image as compared to the PSO case which is earlier reported. Keyword: Swarm optimization, honey bee mating optimization and image enhancement.

The contrast enhancement, brightness preserving and natural look of the output image plays an important role in an infrared (IR) image enhancement process. There are several IR image enhancement algorithms that have been developed, but they may produce artificial looking images, and the images derived from these methods are not suitable for applications such as surveillance, environmental monitoring and so on. In this paper, an algorithm using new top-hat transform and multi-scale sequential toggle operator has been proposed. Firstly, a new white top-hat transform and new black top-hat transform is generated to extract the light and dark regions of the image. Then, the multi-scale sequential toggle operator is used on the extracted light and dark regions to get a sequence of regions. Ultimately, the final image is extracted from a sequence of images. To test the proposed method, the experiment is carried out on the OSU thermal bench mark database images. The enhanced image results have been compared with the popular existing methods such as Histogram Equalization (HE) and Adaptive Histogram Equalization (AHE), max-median (3 × 3) using various evaluation measures. From the results, it is observed that the proposed method works better than the existing methods.

Histogram equalisation has been widely used for image enhancement because of its simple implementation and satisfactory performance. However, traditional histogram equalisation uniformly redistributes an entire histogram or multiple piecewise histograms with the same equalisation strategy, which may produce unnatural artefacts, over‐enhancement or under‐enhancement in wide dynamic range dark image enhancement. This study proposes an adaptive extended piecewise histogram equalisation algorithm (AEPHE) for dark image enhancement. First, an original histogram is divided into a group of extended piecewise histograms. Then, an adaptive histogram equalisation, which balances intensity preservation and contrast boosting, is further developed and respectively applied to these extended piecewise histograms. The final histogram for image enhancement is produced by a weighted fusion of these equalised histograms. The experimental results indicate that AEPHE is superior to multiple state‐of‐the‐art algorithms.

This paper presents a comparative analysis of fundus image enhancement techniques to detect diabetic retinopathy (DR). Medical images frequently suffers from non-uniform illumination, poor contrast and noise, thus these images have to go through pre-processing stage. For image enhancement many techniques are proposed based on spatial domain (histogram). However, these methods usually decline to produce suitable consequences for non-uniform illumination and wide-ranging of low-contract. The comparative analysis and performance evaluation of various enhancement techniques will help in choosing most suitable technique which may significantly improve the detection of diabetic retinopathy. In this paper histogram equalization (HE), adaptive histogram equalization (ADHE), contrast limited adaptive histogram equalization (CLAHE) and exposure based sub-image histogram equalization (ESIHE) techniques are compared for pre-processing of fundus image for detecting DR. For fair analysis of these techniques, histogram, SNR, entropy, absolute mean brightness error and peak signal-to-noise ratio (PSNR) of fundus images are analyzed by using MATLAB.

Color Image Enhancement based on Gamma Encoding and Histogram Equalization

Nowadays, the image's compression presents major interest for storage and communication. This paper deals with DNA's particular images and compression based on frequency and time frequency Transform methods. Three compression methods realized by such Transforms have been studied. The first is based on the Discrete Cosine Transform (DCT), the second used the “Wavelet Transform” and the third is realized by the Curvelet Transform. We have investigated all the methods and adopted them on DNA's images (Chromosome I of C. Elegans). Then we evaluated their performances by calculating the Compression Rate (CR), the Peak Signal to Noise Ratio (PSNR) and also the Structural Similarity (SSIM) Index. We obtained promising results, especially with the Curvelet Transform that was used to improve our approach. CR, PSNR, SSIM reached 93.63, 97.0767 and 1, respectively.

Transform coding has been the focus of the research for image compression. In previous research, the Amplitude and Group Partitioning (AGP) coding scheme is proved to be a low complexity algorithm with higher performance, clearly one of the state-of-art transform coding techniques. However, the previous AGP is used along with the Discrete Cosine Transform (DCT) and the discrete wavelet transform. In this paper, a different transform, the Lapped Orthogonal Transform (LOT), replaces the DCT in conjunction with the AGP. This is the first time LOT and AGP have been combined in a coding method. The definition and design of the LOT are discussed. An objective metric to measure the performance of transform, coding gain, is calculated for both the DCT and the LOT. The LOT has slightly higher coding gain than the DCT. The principles of the LOT based AGP image codec (LOT-AGP) are presented and a complete codec, encoder and decoder, is implemented in software. The performance of the LOT-AGP is compared with other block transform coding schemes: the baseline JPEG codec and the DCT based AGP image codec (DCT- AGP) by both objective evaluation and subjective evaluation. The Peak Signal to Noise Ratio (PSNR) is calculated for these three coding schemes. The two AGP codecs are much better than the JPEG codec on PSNR, from about 1.7 dB to 3 dB depending on bit rate. The two AGP schemes have PSNR differences only to a small degree. Visually, the LOT-AGP has the best-reconstructed images among these three at all bit rates. In addition, the coding results of two other state-of-art progressive image codecs are cited for further comparison. One is the Set Partitioning in Hierarchical Trees (SPIHT) algorithm with a dyadic wavelet transform, and the other is Tran and Nguyen's method with the generalized LOT transform. The AGP coding and the adaptive Huffman entropy coding of LOT-AGP are less complex and the memory usage is smaller than in these two progressive codecs. Comparing these three codecs, i.e. the LOT-AGP and the two progressive codecs in PSNR small only small differences in PSNR. SPIHT has about 1 dB higher PSNR than the LOT-AGP and Tran and Nguyen's method for the test image Lena. For the test image Barbara, the PSNR of the LOT- AGP is about 0.5 dB higher than that of the SPIHT and 0.5 dB lower than that of Tran and Nguyen's method. This low- complexity and high performance codec may provide a new direction for the implementation of image compression.

Mammography is a technique which uses X-rays to take mammographic images of the breast, but identifying abnormalities from a mammogram is a challenging task. Many Computer-Aided Diagnosis (CAD) systems are developed to aid the classification of mammograms, as they search in digitized mammographic images for any abnormalities like masses, microcalcification which is difficult to identify especially in dense breasts. The first step in designing a CAD system is preprocessing. It is the process of improving the quality of the image. This paper focuses on the techniques involved in preprocessing the mammogram images to improve its quality for early diagnosis. Preprocessing involves filtering the image, applying image enhancement techniques like Histogram Equalization (HE), Adaptive Histogram Equalization (AHE), Contrast-Limited Adaptive Histogram Equalization (CLAHE), Contrast Stretching, and Bit-plane slicing; filtering techniques like mean, median, Gaussian and Wiener filters are also applied to the mammogram images. The performance of these image enhancement techniques are evaluated using quality metrics, namely Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Contrast-to-Noise Ratio.

Contrast Enhancement is a technique which comes into the part of Image Enhancement. Contrast Enhancement is used to enhance the visual quality of any captured or other image. Contrast Enhancement can be performed with the help of Histogram equalization (HE). In this technique, the image is collected in the gray scale allocation. The image is then partitioning and applying adaptive Histogram equalization (AHE). Fuzzy logic provides a set of logics which enhance the contrast and visibility of any image. In this technique, the visual quality and the contrast of image will change and then compare these results with previous techniques. The performance of several established image enhancement techniques is presented in terms of different parameters like Absolute mean brightness error (AMBE), Peak signal to noise ratio (PSNR), contrast and Visual quality.

Histogram equalization is a famous method for enhancing the contrast and image features. However, in few cases, it causes the overenhancement, and hence demolishes the natural display of the image. Therefore, in this article, a new fuzzy clustering based subhistogram scheme using discrete cosine transform (DCT) for contrast enhancement has been proposed. For preserving the distinctive appearance of the image, histogram division and separate histogram equalization is done on each subhistogram. The way of dividing histogram and calculating the numbers of parts for histogram division are the major problems which directly affects the quality of the output image. The proposed fuzzy-DCT scheme includes automatic calculation of a number of parts in which histogram is divided. Histogram division has done on the basis of density function and histogram separation is computed in such a way that each main peak can be divided in a different segment. The proposed scheme consists of four stages. The first stage includes the automatic calculation of number of clusters for image brightness levels. The second stage includes clustering of brightness levels by the fuzzy c -means clustering method and utilizing the given transfer function of histogram equalization. In the third stage, contrast enhancement is computed on each individual cluster separately. In the final stage, DCT is employed on the resulting image of the third step for better contrast and brightness preservation. The simulation results of the proposed scheme reveal not only clearer features along with a contrast enhancement, but also remarkably more natural look in the images.

Histogram equalization is an important image enhancement technique widely used in image processing. It is good method of image enhancement indeed; but it doesn't consider about human visual response to light. We know that human visual response to light is non-linear, which is somewhat likes natural logarithmic function. So the gray levels of histogram equalized, image is not really equally distributed for human eyes. In fact, the ordinary histogram equalization is not visually equalized. According to the human visual model, this article puts forward a new algorithm or image histogram modification, with which modified image has visually equal gray level distribution. This algorithm is conducted while assuming exiting a visual image whose histogram can be counted and is equal distributed. Experiment proves that this algorithm is better than ordinary image histogram equalization technique in image enhancement. It fits human visual response well. It can be widely used in digital image processing.

Image enhancement is the process of modifying digital images so that results are suitable for human perception. An upcoming need for image visualization during all lighting conditions by the use of infrared (IR) imagery has gained momentum. It is deemed fit for efficient target acquisition and object deduction. However, due to low image resolution and difficulty in spotting certain objects whose temperature is similar to that of the ground, infrared images must be subjected to further enhancement. Our given proposal aims to enhance infrared images, making use of the fuzzy-based enhancement technique (FBE), and to compare its efficacy with other techniques such as histogram equalization (HE), adaptive histogram equalization (AHE), max–median filter, and multi-scale top-hat transform. The enhanced image is then analyzed using different quantitative metrics such as peak signal-to-noise ratio (PSNR), image quality index (IQI), and structural similarity (SSIM) for performance evaluation. From experimental results, it is concluded that FBE results in the best quality image.

Histogram Equalization (HE) is a simple, effective and widely used contrast enhancement technique as it can automatically define the intensity transformation function based on statistical characteristics of the image, but it tends to change the mean brightness of the image to the middle level of the gray level range. HE also produces saturation effects by extremely pushing the intensities towards the right or the left side of the histogram. To surmount these drawbacks, Clipping or Plateau Histogram Equalization techniques for brightness preserving and contrast enhancement have been proposed, but, these are not suitable for automatic systems because of manual selection of threshold level. Self-Adaptive Plateau Histogram Equalization (SAPHE) selects the threshold level automatically, but the process is relatively complicated and sometimes fails in execution. To overcome these drawbacks, a Modified Self - Adaptive Plateau Histogram Equalization with Mean threshold (Modified SAPHE-M) is proposed in this paper and compared the experimental results with Histogram Equalization (HE), Self-Adaptive Plateau Histogram Equalization (SAPHE) and Modified Self-Adaptive Plateau Histogram Equalization (Modified SAPHE) by using image quality measures such as Absolute Mean Brightness Error (AMBE) and Peak-Signal to Noise Ratio (PSNR).

Digital image processing forms core research area with in computer science disciplines. Rapid growth of image processing technologies has been used digital images more and more prominent in our daily life. Brightness preservation is a technique of improving the image brightness so that the limitations contained in these images is used for various applications in a better way. The contrast enhancement techniques capable to clean up the unwanted noises and enhance the image’s brightness and contrast. In this project aimed to recover the original form of the image and video taken without presence of proper lighting effect. The previous techniques used are histogram equalization, gamma correction for contrast and brightness enhancement in image, here the improved version to overcome the noise and contrast enhancement by the FFT, Bi-log transformation and NTSC are used for both image and video quality improvement.