New Contrast Enhancement Techniques Based On Histogram Equalization Concept For Gray Scale Image

Even though the histogram equalization (HE) is well known for its simplicity and effectiveness in image contrast enhancement, nevertheless, it does suffer from excessive brightness change, intensity saturation and noise amplification problems. In general, the conventional HE based methods are divided into two categories, namely brightness preservation (i.e., designed to preserve the brightness and improve the contrast of the natural images taken under normal lighting condition) and detail preservation (i.e., improve the visual and increase the brightness of the natural images taken under low lighting condition) methods. In this study, a brightness preservation method, namely Improved Quantized Plateau Limits Bi-Histogram Equalization (IQPLBHE) and a detail preservation method, namely Dynamic Range Bi-Histogram Equalization (DRBHE), are introduced. Basically, the proposed IQPLBHE method first separates the input histogram into two sub-histograms. Then, the plateau limits are calculated from the respective sub-histograms, which are used to modify those sub-histograms. Lastly, HE is then separately performed on the two sub-histograms. On the other hand, the proposed DRBHE method first separates the input histogram into two sub-histograms. Then, a new probability density function is created based on local information. Next, cumulative density function normalization is applied. Lastly, a new separating point is calculated before the combination of HE and DRHE is applied. Qualitative and quantitative analyses results show that both the proposed methods have good performance. Moreover, both the proposed methods have the advantages of being simple and tuning free, which are suitable to be applied in consumer electronic products

Improved Image Enhancement Method For Non-Uniform Illumination And Low Ontrast Images Using Bihistogram Modification Approach

Dalam situasi tertentu, imej yang pencahayaan tidak seragam dan beza jelas rendah berkemungkinan akan dirakam. Imej-imej ini dianggap sebagai satu cabaran dalam bidang penglihatan komputer dan pengecaman corak. Teknik-teknik konvensional yang biasa digunakan untuk menyelesaikan masalah ini mempunyai beberapa batasan. Sesetengah teknik memerlukan pelarasan parameter-parameter secara manual. Selain itu, sesetengah teknik hanya tertumpu kepada satu atau dua aspek daripada pengurangan hingar, peningkatan beza jelas, penambahbaikan ketidak seragaman pencahayaan, dan pemeliharaan perincian imej. Oleh itu, penyelidikan ini mencadangkan satu kaedah baharu iaitu " Bi-histogram Modification for Illumination Correction " (BHMIC). Langkah pertama BHMIC ialah membezakan kawasan terang dan gelap dalam sesuatu imej. Kemudian, ia diikuti dengan meningkatkan beza jelas dan keadaan pencahayaan imej tersebut. Pada masa yang sama, proses penyaringan diaplikasi untuk membuang perincian imej (seperti pinggir) dan hingar. Ini dilakukan untuk memelihara perincian tersebut dan menghalang penguatan hingar. Kaedah yang dicadangkan menggunakan anggapan pencahayaan dan pemantulan untuk memisahkan kawasan terang dan gelap imej. Kemudian, kawasan-kawasan tersebut dipertingkatkan dengan menggunakan peningkat terang dan gelap yang diterbitkan secara berasingan. “Clipped Histogram Equalization” yang telah diubahsuaikan kemudiannya digunakan untuk tujuan peningkatan beza jelas. Akhirnya, perincian imej ditambahkan kembali kepada imej yang pencahayaannya diperbetulkan dan beza jelasnya dipertingkatkan untuk menghasilkan imej keluaran akhir. Analisis kualitatif menunjukkan bahawa BHMIC yang dicadangkan mempunyai prestasi yang bagus dalam pemeliharaan butiran imej, peningkatan beza jelas dan penyeragaman keadaan pencahayaan tanpa memperkuatkan hingar yang tidak dikehendaki. Analisis kuantitatif menunjukkan bahawa BHMIC yang dicadangkan adalah 38% hingga 42.9%, 0.8% hingga 4.7% and 0.7% hingga 2.3% lebih baik daripada kaedah-kaedah lain yang telah diuji masing-masing dari segi EME, NIQE dan entropy. Keputusan yang memberangsangkan ini menunjukkan bahawa BHMIC berkemungkinan mampu digunakan sebagai pra-pemprosesan kepada imej wajah untuk pengecaman wajah, imej perubatan untuk memudahkan diagnosis penyakit dan imej fotografi untuk kegunaan peribadi. ________________________________________________________________________________________________________________________ In certain situations, low contrast and non-uniform illuminated images would be captured. These images are considered as challenge in the field of computer vision and pattern recognition. The conventional techniques that are commonly used to solve this problem have some limitations. Some of these methods require manual parameters tuning. Besides that, some of the methods are only focused on one or two aspects of noise reduction, contrast enhancement, non-uniform illumination enhancement and detail preservation. Hence, this study proposes a new method which is Bi-histogram Modification for Illumination Correction (BHMIC). The proposed BHMIC will first distinguish the bright and dark regions of an image. Then, it is followed by enhancing the contrast and illumination condition of the image. At the same time, filtering process is employed in order to remove the details of the image (i.e. edges) and noises. It is done to preserve the details and avoid the amplification of noises. The proposed method applies the illumination and reflectance assumptions to separate the dark and bright regions of the image. Then, these regions are enhanced using derived dark and bright enhancers separately. The modified clipped histogram equalization is then applied for contrast enhancement purpose. Finally, the details of the image are added to the illumination corrected and contrast enhanced image as an output image. Qualitative analysis shows that the proposed BHMIC has good performance in detail preservation, contrast enhancement and illumination condition enhancement without significantly amplifying unwanted noise. The quantitative analysis shows that the proposed BHMIC is 38% to 42.9%, 0.8% to 4.7% and 0.7% to 2.3% better than other tested methods in EME, NIQE and entropy, respectively. The promising results suggest that the proposed BHMIC could probably be used in pre-processing of face images for face recognition, medical images for easier disease symptoms diagnosis and photography images for personal usage

IMPROVEMENT OF DIGITAL IMAGE WITH HISTOGRAM EQUALIZATION METHOD

Digital image has become an inherent thing in everyday life. This is supported by the increasingly sophisticated technology of today where communication devices such as mobile phones have been able to use the role of replacing analog cameras to take pictures or even record videos. Capture the moments of happiness or just want to record an event becomes very easy to do, but sometimes the resulting photo is less satisfactory because of the mobile phone's specifications and other factors such as poor lighting (dark). Efforts to improve become very necessary, but because the application to do this only exists on the computer and not too many are found on mobile phones, this becomes difficult to do. One method for image enhancement is the Histogram Equalization method. This method can be used to improve image quality related to lighting by maintaining color constancy. The use of the histogram equalization method is considered easy because of its simplicity and relatively better performance on almost all types of images. The operation of HE (Histogram Equalization) is carried out by remapping the gray level of the image based on the probability distribution of the gray input level. This flattens and dynamically stretches the various histogram images and results in an overall increase in contrast

Weighted contrast enhancement based enhancement for remote sensing images

This paper discuss a novel approach based on dominant brightness level analysis and adaptive intensity transformation to enhance the contrast for remote sensing images. In this approach  we first perform discrete wavelet (DWT) on the input images and then decompose the bLL sub band into low-, middle-, and high-intensity layers using the log-average luminance. After estimating the intensity transformation, the resulting enhanced image is obtained by using the inverse DWT. The proposed algorithm overcomes this problem using the adaptive intensity transfer function. The experimental results show that the proposed algorithm enhances the overall contrast and visibility of local details better than existing techniques