Video denoising is fundamental to low-level vision and real-world imaging, yet existing self-supervised methods remain fragile under severe noise and complex motion. Most approaches still rely on spatially and temporally discrete grid-based representations: blind-spot networks enforce J-invariance by masking center pixels with a limited receptive field, while recurrent models build temporal dependencies on discretized frame sequences and noise-sensitive optical flow, leading to error accumulation and motion artifacts. We address this model bottleneck by reformulating self-supervised video denoising as learning a continuous spatiotemporal implicit field. Building on coordinate-based implicit neural representations, we propose a unified video denoising model with a spatiotemporal implicit neural field (SINF). In the spatial domain, a blind-spot implicit spatial field maps coordinates directly to pixel-level representations, enabling globally informed texture recovery beyond receptive-field limits. In the temporal domain, an implicit temporal embedding with periodic activations encodes motion continuously over time, while a time-aware spatial graph module refines cross-frame alignment. Together, SINF remodels discretized video signals into a continuous spatiotemporal intensity field, enabling more robust pixel-wise associations than coarse optical flow. Extensive experiments on synthetic and real noisy video benchmarks demonstrate that our SINF achieves state-of-the-art performance on synthetic and real noisy video benchmarks.

Most recently, learned image compression methods have outpaced traditional hand-crafted standard codecs. However, their inference typically requires to input the whole image at the cost of heavy computing resources, especially for high-resolution image compression; otherwise, the block artefact can exist when compressed by blocks within existing learned image compression methods. To address this issue, we propose a novel continuous patch stitching (CPS) framework for block-wise image compression that is able to achieve seamlessly patch stitching and mathematically eliminate block artefact, thus capable of significantly reducing the required computing resources when compressing images. More specifically, the proposed CPS framework is achieved by padding-free operations throughout, with a newly established parallel overlapping stitching strategy to provide a general upper bound for ensuring the continuity. Upon this, we further propose functional residual blocks with even-sized kernels to achieve down-sampling and up-sampling, together with bottleneck residual blocks retaining feature size to increase network depth. Experimental results demonstrate that our CPS framework achieves the state-of-the-art performance against existing baselines, whilst requiring less than half of computing resources of existing models. The source code and trained models are available athttps://github.com/bblgbr/SPL-CPS.