Block discrete cosine transform (BDCT) is an indispensable component of modern image and video coding standards, specifically for its decorrelation and superior energy compaction aspects. BDCT typically employs block-specific quantization, which results in unpleasant compression-blocking artifacts which predominates at low bit rates. The proposed method aims to minimize these blocking artifacts to generate high-quality images under the framework of the alternating direction method of multipliers (ADMM) optimization. The proposed method exploits the local structures identified and extracted via wavelet-patch-based sparse representation and non-local self-similarity identified and extracted via group-based sparse representation, which are subsequently combined optimally employing ADMM. Moreover, the method uses a Gaussian quantization noise model, which allows a more precise and reliable assessment. An adaptive regularization parameter is used, which integrates spectral and spatial domain sparse representations with multi-resolution dictionaries and PCA-based dictionary. The proposed algorithm improves the overall practicality of the process and outperforms existing methods in terms of objective measures like structural similarity index measure, peak signal-to-noise ratio and visual perception.

This paper presents a hardware architecture for 8 x 8 2D Discrete Cosine Transform (DCT) and Inverse DCT (IDCT) using Taylor-series expansion of trigonometric functions. The processing of DCT/IDCT is modified to eliminate the need for: 1) partitioning the input image into fixed-size blocks and 2) use of transpose buffer for storing intermediate results, achieving low clock cycle time compared to existing techniques. The values obtained from Taylor-series are approximated by fixed-point numbers that reduce the hardware complexity at the cost of acceptable loss in quality of the output image. Based on the fixed-point approach, 18-bit, 24-bit, and 32-bit DCT and IDCT architectures are proposed. This DCT architecture considers the implementation of all 64 coefficients and hence, it is named as DCT64. An important observation about 2D DCT is that most of the compaction energy is concentrated on the low-frequency DCT coefficients which are sufficient to reconstruct the original image. Taking this point into consideration, three DCT architectures, namely, DCT15, DCT10, and DCT6 with 15, 10, and 6 coefficients are designed which are compared with the DCT64. The percentage improvement in the area and performance utilization is obtained for DCT15, DCT10, and DCT6 against DCT64. A gradual increase in the percentage is observed from DCT15 through DCT6 with a marginal decline in the quality of the output image at each step which can be seen from the measured PSNR and SSIM values. Regarding IDCT, the proposed architecture achieved a low percentage of area utilization and similar performance to that of the DCT. The field-programmable gate array (FPGA) implementation of the proposed DCT architecture operates at a higher frequency, achieving lower values of dynamic power, slice reg, slice LUTs metrics that outperforms the processing capabilities of existing Algebraic Integer (AI) and fixed-point implementations.

Coherence enhancing shock filters combine shock filtering with the orientation estimation of the structure tensors thus enhancing the coherent flow‐like structures. The basic operations defined here are dilation and erosion that take place in the zones of influence. However, in order to achieve the goal of texture enhancement, in the proposed method, the authors have extended this notion to define an opening and closing based shock filter. Subsequently, the open–close filtered image is employed to locate and highlight the bright and dark texture features over the entirety of the image. Combining these feature images with the original image in a specific way will produce an image with texture features enhanced. Furthermore, we have performed these operations at different scales to achieve better enhancement of the texture features. The method has been formulated, implemented and tested over a number of synthetic and natural texture images and the experimental results establish the efficacy of the proposed method in enhancing the prominent texture parts in the image proportionately more than the non‐prominent texture parts.

Iris scans are most promising, secure and stable than any other biometrics such as fingerprints etc. However, the Iris Recognition Systems (IRS) are exposed to the possibility of being attacked by spoofing using contact lenses. Consequently, detecting the presence of contact lenses becomes obligatory for an IRS to work meticulously. The main contribution of this paper is to present a transfer learning technique relying on a pre-trained deep Convolutional Neural Network (CNN) to extract features followed by principal component analysis (PCA) based feature selection. At last, a cubic Support Vector Machine (cSVM) is used for training Error-Correcting Output Code (ECOC) multiclass model to detect the presence and type of a contact lens. The pre-learnt CNN architecture used here is trained on a huge size of dataset which can be transferred to contact lens detection with a smaller sized dataset. The proposed method is evaluated on two publicly available databases namely, IIIT-D and ND in order to ascertain the adaptability of our method. The comparative analysis affirms the performance superiority (i.e., correct classification rate (CCR)) of the proposed method over the state-of-the-art contact lens detection algorithms.

In this paper, the authors have proposed a method of segmenting gray level images using multiscale morphology. The approach resembles the watershed algorithm in the sense that the dark (respectively bright) features which are basically canyons (respectively mountains) on the surface topography of the gray level image are gradually filled (respectively clipped) using multiscale morphological closing (respectively opening) by reconstruction with isotropic structuring element. The algorithm detects valid segments at each scale using three criteria namely growing, merging and saturation. Segments extracted at various scales are integrated in the final result. The algorithm is composed of two passes preceded by a preprocessing step for simplifying small scale details of the image that might cause over-segmentation. In the first pass feature images at various scales are extracted and kept in respective level of morphological towers. In the second pass, potential features contributing to the formation of segments at various scales are detected. Finally the algorithm traces the contours of all such contributing features at various scales. The scheme after its implementation is executed on a set of test images (synthetic as well as real) and the results are compared with those of few other standard methods. A quantitative measure of performance is also formulated for comparing the methods.