On the Use of Normalized Compression Distances for Image Similarity Detection.

This paper investigates the usefulness of the normalized compression distance (NCD) for image similarity detection. Instead of the direct NCD between images, the paper considers the correlation between NCD based feature vectors extracted for each image. The vectors are derived by computing the NCD between the original image and sequences of translated (rotated) versions. Feature vectors for simple transforms (circular translations on horizontal, vertical, diagonal directions and rotations around image center) and several standard compressors are generated and tested in a very simple experiment of similarity detection between the original image and two filtered versions (median and moving average). The promising vector configurations (geometric transform, lossless compressor) are further tested for similarity detection on the 24 images of the Kodak set subject to some common image processing. While the direct computation of NCD fails to detect image similarity even in the case of simple median and moving average filtering in 3 × 3 windows, for certain transforms and compressors, the proposed approach appears to provide robustness at similarity detection against smoothing, lossy compression, contrast enhancement, noise addition and some robustness against geometrical transforms (scaling, cropping and rotation).

Embedding complementary imaging data in laser scanning microscopy micrographs by reversible watermarking.

Complementary laser scanning microscopy micrographs are considered as pairs consisting in a master image (MI) and a slave image (SI), the latter with potential for facilitating the interpretation of the MI. We propose a strategy based on reversible watermarking for embedding a lossy compressed version of the SI into the MI. The use of reversible watermarking ensures the exact recovery of the host image. By storing and/or transmitting the watermarked MI in a single file, the information contained in both images that constitute the pair is made available to a potential end-user, which simplifies data association and transfer. Examples are presented using support images collected by two complementary techniques, confocal scanning laser microscopy and transmission laser scanning microscopy, on Hematoxylin and Eosin stained tissue fragments. A strategy for minimizing the watermarking distortions of the MI, while preserving the content of the SI, is discussed in detail.

Adaptive Pairing Reversible Watermarking.

This letter revisits the pairwise reversible watermarking scheme of Ou et al., 2013. An adaptive pixel pairing that considers only pixels with similar prediction errors is introduced. This adaptive approach provides an increased number of pixel pairs where both pixels are embedded and decreases the number of shifted pixels. The adaptive pairwise reversible watermarking outperforms the state-of-the-art low embedding bit-rate schemes proposed so far.

On local prediction based reversible watermarking.

The use of local prediction in difference expansion reversible watermarking provides very good results, but at the cost of computing for each pixel a least square predictor in a square block centered on the pixel. This correspondence investigates the reduction of the mathematical complexity by computing distinct predictors not for pixels, but for groups of pixels. The same predictors are recovered at detection. Experimental results for the case of prediction on the rhombus defined by the four horizontal and vertical neighbors are provided. It is shown that by computing a predictor for a pair of pixels, the computational cost is halved without any loss in performance. A small loss appears for groups of three and four pixels with the advantage of reducing the mathematical complexity to a third and a fourth, respectively.

Local-prediction-based difference expansion reversible watermarking.

This paper investigates the use of local prediction in difference expansion reversible watermarking. For each pixel, a least square predictor is computed on a square block centered on the pixel and the corresponding prediction error is expanded. The same predictor is recovered at detection without any additional information. The proposed local prediction is general and it applies regardless of the predictor order or the prediction context. For the particular cases of least square predictors with the same context as the median edge detector, gradient-adjusted predictor or the simple rhombus neighborhood, the local prediction-based reversible watermarking clearly outperforms the state-of-the-art schemes based on the classical counterparts. Experimental results are provided.

Low distortion transform for reversible watermarking.

This paper proposes a low-distortion transform for prediction-error expansion reversible watermarking. The transform is derived by taking a simple linear predictor and by embedding the expanded prediction error not only into the current pixel but also into its prediction context. The embedding ensures the minimization of the square error introduced by the watermarking. The proposed transform introduces less distortion than the classical prediction-error expansion for complex predictors such as the median edge detector or the gradient-adjusted predictor. Reversible watermarking algorithms based on the proposed transform are analyzed. Experimental results are provided.

Automated compensation of light attenuation in confocal microscopy by exact histogram specification.

Confocal laser scanning microscopy (CLSM) enables us to capture images representing optical sections on the volume of a specimen. The images acquired from different layers have a different contrast: the images obtained from the deeper layers of the specimen will have a lower contrast with respect to the images obtained from the topmost layers. The main reasons responsible for the effects described above are light absorption and scattering by the atoms and molecules contained in the volume through which the light passes. Also light attenuation can be caused by the inclination of the observed surface. In the case of the surfaces that have a steep inclination, the reflected light will have a different direction than the one of the detector. We propose a technique of digital image processing that can be used to compensate the effects of light attenuation based on histogram operations. We process the image series obtained by CLSM by exact histogram specification and equalization. In this case, a strict ordering among pixels must be induced in order to achieve the exact histogram modeling. The processed images will end up having exactly the specified histogram and not a histogram with a shape that just resembles to the specified one, as in the case of classical histogram specification algorithms. Experimental results and theoretical aspects of the induced ordering are discussed, as well as a comparison between several histogram modeling techniques with respect to the processing of image series obtained by confocal microscopy.

Exact histogram specification.

While in the continuous case, statistical models of histogram equalization/specification would yield exact results, their discrete counterparts fail. This is due to the fact that the cumulative distribution functions one deals with are not exactly invertible. Otherwise stated, exact histogram specification for discrete images is an ill-posed problem. Invertible cumulative distribution functions are obtained by translating the problem in a K-dimensional space and further inducing a strict ordering among image pixels. The proposed ordering refines the natural one. Experimental results and statistical models of the induced ordering are presented and several applications are discussed: image enhancement, normalization, watermarking, etc.