Sujit Kumar Sahoo

Swapna Pillai, Siddharth Singh Savner, Sujit Kumar Sahoo

This letter introduces a physics-informed self-supervised framework for sonar image despeckling that reformulates despeckling as residual consistency in the homomorphic log domain. By constraining the log-ratio residual to obey multiplicative speckle statistics, the proposed method eliminates the need for clean supervision while preventing degenerate identity solutions. A variance-targeted statistical loss combined with edge-aware structural regularization and median-guided curriculum stabilization enables effective speckle suppression with preserved structural fidelity. This formulation along with a lightweight neural network achieves state-of-the-art performance across multiple real sonar datasets and demonstrates excellent cross-dataset robustness, while remaining suitable for real-time deployment.

Prateek Goyal, Jatin Kumar Reddy Mothe, Swara Rajesh Shelke et al.

Floating-point square-root computation is a power- and delay-critical operation in edge-AI, signal-processing, and embedded systems. Conventional implementations typically rely on multipliers or iterative pipelines, resulting in increased hardware complexity, switching activity, and energy consumption. This work presents E2AFS, a lightweight and fully multiplier-free floating-point square-root architecture optimized for energy-efficient computation. By reducing logic depth and minimizing switching activity, the proposed design achieves substantial improvements in hardware efficiency and performance. FPGA implementation on an Artix-7 device demonstrates that E2AFS achieves the lowest dynamic power (7.63 mW), the shortest critical-path delay (4.639 ns), and the minimum power-delay product (35.39 pJ) compared to existing ESAS and CWAHA architectures. Error evaluation using multiple accuracy metrics, together with graphical analysis, shows that E2AFS closely approximates the exact square-root function with consistently low deviation. Application-level validation in Sobel edge detection and K-means color quantization further confirms its suitability for low-power real-time edge and embedded platforms.

Sujit Kumar Sahoo

In this paper the problem of image restoration (denoising and inpainting) is approached using sparse approximation of local image blocks. The local image blocks are extracted by sliding square windows over the image. An adaptive block size selection procedure for local sparse approximation is proposed, which affects the global recovery of underlying image. Ideally the adaptive local block selection yields the minimum mean square error (MMSE) in recovered image. This framework gives us a clustered image based on the selected block size, then each cluster is restored separately using sparse approximation. The results obtained using the proposed framework are very much comparable with the recently proposed image restoration techniques.