Exact Histogram Specification Optimized for Structural Similarity

An exact histogram specification (EHS) method modifies its input image to have a specified histogram. Applications of EHS include image (contrast) enhancement (e.g., by histogram equalization) and histogram watermarking. Performing EHS on an image, however, reduces its visual quality. Starting from the output of a generic EHS method, we maximize the structural similarity index (SSIM) between the original image (before EHS) and the result of EHS iteratively. Essential in this process is the computationally simple and accurate formula we derive for SSIM gradient. As it is based on gradient ascent, the proposed EHS always converges. Experimental results confirm that while obtaining the histogram exactly as specified, the proposed method invariably outperforms the existing methods in terms of visual quality of the result. The computational complexity of the proposed method is shown to be of the same order as that of the existing methods. Index terms: histogram modification, histogram equalization, optimization for perceptual visual quality, structural similarity gradient ascent, histogram watermarking, contrast enhancement

Reversible Watermarking Using Statistical Information

In most reversible watermarking methods, a compressed location map is exploited in order to ensure reversibility. Besides, in some methods, a header containing critical information is appended to the payload for the extraction and recovery process. Such schemes have a highly fragile nature; that is, changing a single bit in watermarked data may prohibit recovery of the original host as well as the embedded watermark. In this paper, we propose a new scheme in which utilizing a compressed location map is completely removed. In addition, the amount of auxiliary data is decreased by employing the adjacent pixels information. Therefore, in addition to quality improvement, independent authentication of different regions of a watermarked image is possible

Maximum Entropy Principle in Image Restoration

Many imaging systems are faced with the problem of estimating a true image from a degraded dataset. In such systems, the image degradation is translated into a convolution with a Point Spread Function (PSF) and addition of noise. Often, the image recovery by inverse filtering is not possible because the PSF matrix is ill-conditioned. Maximum Entropy (MaxEnt) is an alternative method, which uses the entropy concept for estimating the true image. This paper presents MaxEnt method, starting with the historical references of the entropy concept and finalizing with its application in image restoration and reconstruction. The statistical model of MaxEnt for images is discussed and the connection of MaxEnt with the Bayesian inference is explained. MaxEnt is evaluated by using a modified version of Cornwell algorithm. Two cases are considered: images degraded by various PSF kernels in presence of additive noise and images resulted from incomplete datasets. The tests show PSNR gains ranging from 1 to 7dB for the degraded images and images reconstructed at 25dB from datasets with up to 80% missing pixels