Visuo-tactile perception aims to understand an object's tactile properties. However, the field remains underexplored due to the high cost of data collection. We observe that visually distinct objects can exhibit similar surface textures or material properties. For example, a leather sofa and a leather jacket can share similar tactile properties. This implies that tactile understanding can be guided by material cues in visual data, even without direct tactile supervision. In this paper, we introduce RA-Touch, a retrieval-augmented framework that improves visuo-tactile perception by leveraging visual data enriched with tactile semantics. We carefully recaption a large-scale visual dataset with tactile-focused descriptions, enabling the model to access tactile semantics typically absent from conventional visual datasets. A key challenge remains in effectively utilizing these tactile-aware external descriptions. RA-Touch addresses this by retrieving visual-textual representations aligned with tactile inputs and integrating them to focus on relevant textural and material properties. By outperforming prior methods, we demonstrate the potential of retrieval-based visual reuse for tactile understanding. Code is available at https://aim-skku.github.io/RA-Touch.

In this letter, we present an implementation of deblurring hardware to mitigate blur incurred by optical aberrations in a real-time manner to increase resolution for mobile camera modules. As optical aberrations tend to be variant according to spatial location, algorithm should support spatially variant deblurring. Also, a deblurring problem is known to be rank-deficient, thus requires a regularization and a sparse norm such as Lpnorm where 0 ≤ p ≤ 1 has achieved satisfactory solutions. We focus on elaborating algorithm details and a corresponding architecture to efficiently implement with those two requirements. Also, we present a simulation result with the real sensor and this result shows that image resolution can be increased in terms of Modulated Transfer Function (MTF).