Light field (LF) angular super-resolution (SR) aims to reconstruct a densely sampled LF from a sparse input. A key challenge is the large disparity between sparse views, which disrupts angular consistency and makes capturing cross-view correlations difficult. Disparity-based methods often yield errors in textureless, o...
A dual representation-based LFVS method that employs deformable convolutional and Deep Residual Channel Attention (DRCA) networks that achieves state-of-the-art performance on synthetic and real-world LF benchmarks.
Light field imaging captures both spatial and angular information but suffers from an inherent trade-off that limits per-view spatial resolution. This work addresses center-view super-resolution (SR) from multi-view inputs and reveals that naive view aggregation fails to effectively exploit angular information, even as...
Dense light field acquisition provides many angular observations of the same scene, but using all available views can introduce redundancy, disparity-related inconsistency, and unnecessary computational cost. This paper addresses the problem of minimal angular sampling for arbitrary-view light field reconstruction. Ins...
Low-light image super-resolution aims to recover normal-light high-resolution images from dark low-resolution observations captured by image sensors, in which illumination attenuation, sensor noise, blur, and low resolution are entangled, making it more challenging than conventional super-resolution. Diffusion-based me...
Photon noise arising during image acquisition remains a major obstacle to resolving fine structures in optical microscopy, particularly under low-light conditions. Due to the inherent difficulty to obtain noise-free ground truth data, selfsupervised denoising approaches have been widely adopted for microscopy imaging....
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