Due to the limitations of image-capturing devices or the presence of a non-ideal environment, the quality of digital images may get degraded. In spite of much advancement in imaging science, captured images do not always fulfill users' expectations of clear and soothing views. Most of the existing methods mainly focus on either global or local enhancement that might not be suitable for all types of images. These methods do not consider the nature of the image, whereas different types of degraded images may demand different types of treatments. Hence, we classify images into several classes based on the statistical information of the respective images. Afterwards, an adaptive gamma correction (AGC) is proposed to appropriately enhance the contrast of the image where the parameters of AGC are set dynamically based on the image information. Extensive experiments along with qualitative and quantitative evaluations show that the performance of AGC is better than other state-of-the-art techniques.

This paper compares brightness preserving image enhancement techniques using bi-histogram equalization and tri-histogram equalization methods. Traditionally for image contrast enhancement, global histogram equalization technique is used extensively. However, global histogram equalization tends to change the mean brightness of any image to the middle gray level of the dynamic range, which often results in over or under enhancement and introduce some annoying artifacts. To overcome such problems, several bi-histogram based techniques and tri-histogram based technique has been proposed. While bi-histogram based techniques divides the histogram of any image into two subhistograms and equalize them independently, tri-histogram based technique divides the histogram into three sub histograms. This paper compares some of these equalization techniques. Simulation results can be quantitative and qualitative in nature. For quantitative analysis, Absolute Mean Brightness Error (AMBE) measurement has been used. And qualitative results can be observed from the image itself.