While interest in blending sound with culinary experiences has grown in Human-Food Interaction (HFI), the significance of food's material properties in shaping sound-related interactions has largely been overlooked. This paper explores the opportunity to enrich the HFI experience by treating food not merely as passive nourishment but as an integral material in computational architecture with input/output capabilities. We introduce “Sonic Delights,” where food is a comestible auditory-gustatory interface to enable users to interact with and consume digital sound. This concept redefines food as a conduit for interactive auditory engagement, shedding light on the untapped multisensory possibilities of merging taste with digital sound. An associated study allowed us to articulate design insights for forthcoming HFI endeavors that seek to weave food into multisensory design, aiming to further the integration of digital interactivity with the culinary arts.

Clear and high-resolution (HR) underwater images are indispensable in acquiring underwater information. However, existing underwater image enhancement and super-resolution (UIESR) networks achieve limited enhancement-super-resolution performance on real-world turbid low-resolution (LR) underwater images because (1) they assume that the resolution degradation is simple and known bicubic down-sampling, generating unrealistic training data for UIESR task; (2) they extract known priors from the underwater imaging model, which is meager to address complex UIESR problems caused by unknown mixed dual-degradation; and (3) they ignore the interaction between blurring and color casts in the RGB color space, leading to unsatisfactory correction results of two distortions. To address these issues, we propose a realistic UIESR network (RUIESR) consisting of three parts: a realistic LR image generation module (RLGM), a dual-degradation estimation module (DEM), and an enhancement and super-resolution module (ESRM). Firstly, RLGM aims to generate LR images obeying underwater LR image distribution by learning real LR properties from unpaired real LR-HR underwater images for training. Secondly, a contrast-driven learning strategy is proposed in the DEM to accurately estimate unknown dual-degradation priors that can aid the reconstruction task. Finally, ESRM is proposed to enhance textures and correct color casts, which includes a dual-branch structure to separate blurring and color casts distortions and utilizes specific priors for each distortion to assist reconstruction. Extensive experiments on real and synthetic underwater datasets show that the proposed RUIESR outperforms existing works regarding visual quality and quantitative metrics.

Image acquisition and reconstruction play an important role in underwater detections and explorations. However, the limited underwater acoustic communication channels and narrow bandwidth resources will have a great impact on the performance of the traditional data acquisition methods, resulting in the loss of details and blur in reconstructed underwater images. Compressive sensing theory (CS) which can reconstruct images from fewer measurement than that required by Nyquist sampling law has been proved to have good effect on image sampling and reconstruction. Nevertheless, the existing CS methods are not suitable for underwater images because most of them are designed for on-land images which have huge differences from underwater images. In this paper, we propose a novel priors guided adaptive underwater compressive sensing framework, dubbed UCSNet, which can effectively sample and reconstruct underwater images under a fixed low sampling ratio. In particular, our framework is composed of three sub-networks: underwater priors extraction and guidance network (UEGN), sampling matrix generation network (SMGNet) and channel-wise reconstruction network (CWRNet). Specifically, inspired by the underwater imaging physical models, UEGN is designed to extract features of underwater priors information and combine them adaptively. UEGN also introduce the imaging process into CS task to make sampling and reconstruction consistent with underwater imaging characteristics. SMGNet uses underwater content degradation to assist the analysis of structural information to generate sampling matrices. Considering the monotony of color tones caused by light absorption in underwater images, CWRNet embedded with the channel-wise module (CWM) is designed to enforce the whole network to allocate different number of sampling points on luminance and chrominance channels respectively and make feature maps extracted from them complement with each other. Experimental results demonstrate that our proposed framework can achieve both PSNR and SSIM gains on underwater images reconstruction quality and have greater visual quality than other state-of-art methods under fixed sampling ratios.