Abstract The main challenge in content-based image retrieval (CBIR) systems is accurately describing image features in a manner that aligns with human perception. Natural images consist of varying luminance levels and complex, intertwined texture patterns. Decomposing an image into its constituent components can simplify feature extraction and enhance system performance. This paper proposes a method for CBIR based on luminance and texture decomposition. In the luminance component, texture features are suppressed, allowing luminance features to be extracted using AlexNet. Conversely, in the texture component, texture features are amplified and extracted using SqueezeNet. AlexNet captures the global context of images by utilizing spatial information, thereby enhancing feature contrast for better discrimination between object classes. SqueezeNet is selected for its ability to produce compact, highly discriminative feature vectors that effectively describe texture features. To mitigate redundancy caused by feature overlap, the most relevant features are selected using the Boruta–Shap algorithm. The feature space is visualized using the t-distributed stochastic neighbor embedding (t-SNE) technique, and the interpretability of the proposed approach is evaluated through Shapley value analysis. Experimental results demonstrate the effectiveness of the proposed approach in improving CBIR performance.

Abstract Image steganography is the process of concealing secret information within a cover image. The main challenge of steganography is to ensure that the embedding process does not significantly alter the cover file. In this paper, instead of modifying a cover image to carry information, steganography is performed using a set of images. These images are selected from a dataset of natural images. Each image in the dataset is divided into a number of non-overlapping patches. Then, indexing of the patches is performed based on their features. The secret image is also divided into a set of non-overlapping patches. Similar versions of the patches in the secret image are searched in the dataset to identify candidate patches. The final candidate is selected by calculating the minimum distance between the feature vector of the patches in the secret image and the patches in the dataset. Finally, the receiver retrieves the secret image using the pieces of selected images. Since, instead of embedding information in a cover image, a set of patches from natural images are selected without any changes, this approach can resist change-tracking tools, as demonstrated by experimental results, and also offers the advantage of high embedding capacity.

The quality of the image formed by an optical system is reduced by aberrations. This paper points out and attempts to correct blind of lens aberration. To this end Zernike moments introduced for presenting lens aberration model within which their coefficients are estimated through polyspectral analysis. The model parameters are divided into asymmetric and symmetric which are estimated through bicoherence and tricoherence respectively. The obtained precision compares favorably to the aberration given by state of the art ployspectral analysis and reaches a RMSE of 0.1 pixels.