Accurate and non-invasive optical metrology of transparent objects is essential in a wide range of scientific and industrial applications, from fluid dynamics to biomedical imaging. In this work, a digital holography approach for temperature mapping is presented that leverages a double field of view (FOV) configuration combined with high spatial bandwidth utilization (SBU). By capturing a multiplexed hologram from two distinct objects in a single-shot, the system overcomes the limitations inherent to single-view holography, enabling a more comprehensive object information of temperature-induced refractive index variations. The method integrates double FOV digital holography with high SBU, allowing for the accurate surface profiling and mapping of complex optical path length changes caused by temperature gradients. The technique exhibits strong potential for applications in glass industry and microfluidic thermometry, convection analysis, and combustion diagnostics, where precise thermal field measurements are crucial. Overall, this study introduces an efficient holographic framework that advances the capabilities of non-contact measurement applications by integrating double FOV acquisition in a single-shot with enhanced spatial bandwidth exploitation. The approach sets the groundwork for real-time, volumetric thermal imaging and expanding the applicability of digital holography in both research and industrial settings.

Digital holographic microscopy (DHM) involves computationally demanding operations such as numerical wave propagation, phase retrieval, and three-dimensional (3D) reconstruction, which rely heavily on large-scale matrix computations. As spatial resolution and sampling density increase, the associated computational burden grows substantially, often necessitating optimized signal-processing strategies to enable real-time and high-fidelity reconstruction. To address this, we propose a sparse reconstruction method based on adaptive dictionary learning (DL) named as Sequential Generalized K-means (SGK) is integrated into DHM. In the proposed method, input hologram is divided into patches, and each patch is represented as linear combination of sparse dictionary atoms. Each atom in dictionary is updated sequentially by averaging the training patches associated with its fixed sparse coefficients. Unlike K-SVD, proposed method avoids the expensive singular value decomposition (SVD) for dictionary update thereby minimizes the computation time and improves the hologram reconstruction accuracy. To further enhance reconstruction quality, optimum choice of key parameters such as patch size, dictionary size, and sparsity level is essential. Experimental results demonstrate that the proposed SGK-based sparse reconstruction achieves high-fidelity reconstructions and faster convergence than conventional dictionary-learning approaches, highlighting its suitability for real-time microscopic imaging applications.